Introduction

INSERT ABSTRACT

Our final taxa used in this anaylsis: - included from Bacteria, Archaea, Eukaryota and viruses - removed Chordata, Arthropoda, Cnidaria, Porifera, Echinodermata, Streptophyta, Platyhelminthes because they are implausible in our biological system (pig gut microbiome) - remove genera/phyla that have more than 20 zeroes/33.33% missing values across our dataset of n=60

In the end: Our final dataset has 45 phyla and 755 genera.

Load libraries

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

if (!requireNamespace("remotes", quietly = TRUE))
    install.packages("remotes")

if (!requireNamespace("devtools", quietly = TRUE))
    install.packages('devtools')

BiocManager::install("ALDEx2")
## Warning: package(s) not installed when version(s) same as current; use `force = TRUE` to
##   re-install: 'ALDEx2'
BiocManager::install("phyloseq")
## Warning: package(s) not installed when version(s) same as current; use `force = TRUE` to
##   re-install: 'phyloseq'
remotes::install_github("cpauvert/psadd")
BiocManager::install("multtest")
## Warning: package(s) not installed when version(s) same as current; use `force = TRUE` to
##   re-install: 'multtest'
library(devtools)

devtools::install_github("gauravsk/ranacapa")
# analysis packages
library(ALDEx2) # for univariate analysis
library(rstatix) # for ANOVA
library(vegan) # for beta and alpha diversity
## Warning: package 'permute' was built under R version 4.0.5
library(phyloseq) # for krona plots and rarefaction curves
library(psadd) # additions to phyloseq package for microbiome analysis
library(ranacapa) # Utility Functions  for Simple Environmental Visualizations

# functionality packages
library(data.table) # for nicer transposing
library(here) # for directory management
library(knitr) # for knitting and for kable()
library(tidyverse) # for wrangling and plotting
## Warning: package 'tidyr' was built under R version 4.0.5
## Warning: package 'dplyr' was built under R version 4.0.5
library(readxl) # for reading Excel files

Set seed

Some of our analyses include permutations, so let’s set a seed so we get consistent results each time we run.

set.seed(2021) # hoping this seed is better than 2020 :)

Read in metadata

Input files can be found as supplementary information in:

  • UPDATE WITH MALLORY’S PAPER INFO

The data read in chunk below enables loading our data without any outside-of-R handling. In “Metadata” tab of Supplementary Information.

# upload metadata
AllSamples.Metadata <- read_excel("../Goggans_etal_2021_tomato_pig_microbiome_WGS.xlsx",
                                       sheet = "TableS2.SampleMetadata")

str(AllSamples.Metadata)
## tibble [60 × 5] (S3: tbl_df/tbl/data.frame)
##  $ Sample_Name       : chr [1:60] "ShotgunWGS-ControlPig6GutMicrobiome-Day14" "ShotgunWGS-ControlPig8GutMicrobiome-Day0" "ShotgunWGS-ControlPig3GutMicrobiome-Day14" "ShotgunWGS-TomatoPig14GutMicrobiome-Day7" ...
##  $ Pig               : num [1:60] 6 8 3 14 5 18 16 10 2 18 ...
##  $ Diet              : chr [1:60] "Control" "Control" "Control" "Tomato" ...
##  $ Time_Point        : chr [1:60] "Day 14" "Day 0" "Day 14" "Day 7" ...
##  $ Diet_By_Time_Point: chr [1:60] "Control Day 14" "Control Day 0" "Control Day 14" "Tomato Day 7" ...
# convert Pig, Diet, Time_Point, Diet_By_Time_Point to factors
# and set levels/order
AllSamples.Metadata$Pig <- as.factor(AllSamples.Metadata$Pig)
AllSamples.Metadata$Diet <- as.factor(AllSamples.Metadata$Diet)
AllSamples.Metadata$Time_Point <- factor(AllSamples.Metadata$Time_Point,
                                         levels = c("Day 0", "Day 7", "Day 14"))
AllSamples.Metadata$Diet_By_Time_Point <- 
  factor(AllSamples.Metadata$Diet_By_Time_Point,
         levels = c("Control Day 0", 
                  "Control Day 7", 
                  "Control Day 14", 
                  "Tomato Day 0", 
                  "Tomato Day 7", 
                  "Tomato Day 14"))

# check
str(AllSamples.Metadata)
## tibble [60 × 5] (S3: tbl_df/tbl/data.frame)
##  $ Sample_Name       : chr [1:60] "ShotgunWGS-ControlPig6GutMicrobiome-Day14" "ShotgunWGS-ControlPig8GutMicrobiome-Day0" "ShotgunWGS-ControlPig3GutMicrobiome-Day14" "ShotgunWGS-TomatoPig14GutMicrobiome-Day7" ...
##  $ Pig               : Factor w/ 20 levels "1","2","3","4",..: 6 8 3 14 5 18 16 10 2 18 ...
##  $ Diet              : Factor w/ 2 levels "Control","Tomato": 1 1 1 2 1 2 2 1 1 2 ...
##  $ Time_Point        : Factor w/ 3 levels "Day 0","Day 7",..: 3 1 3 2 2 2 2 2 1 1 ...
##  $ Diet_By_Time_Point: Factor w/ 6 levels "Control Day 0",..: 3 1 3 5 2 5 5 2 1 4 ...

Genera-level annotation

Read in genera level data, annotated from MG-RAST. In “Genera” tab of Supplementary Information.

Genus.AllSamples.Counts <- read_excel("../Goggans_etal_2021_tomato_pig_microbiome_WGS.xlsx",
                                       sheet = "TableS4.Genera")

str(Genus.AllSamples.Counts)
## tibble [1,085 × 66] (S3: tbl_df/tbl/data.frame)
##  $ domain                                    : chr [1:1085] "Viruses" "Bacteria" "Eukaryota" "Bacteria" ...
##  $ phylum                                    : chr [1:1085] "unclassified (derived from Viruses)" "Firmicutes" "unclassified (derived from Eukaryota)" "Cyanobacteria" ...
##  $ class                                     : chr [1:1085] "unclassified (derived from Viruses)" "Bacilli" "unclassified (derived from Eukaryota)" "unclassified (derived from Cyanobacteria)" ...
##  $ order                                     : chr [1:1085] "Caudovirales" "Lactobacillales" "unclassified (derived from Eukaryota)" "unclassified (derived from Cyanobacteria)" ...
##  $ family                                    : chr [1:1085] "Podoviridae" "Aerococcaceae" "unclassified (derived from Eukaryota)" "unclassified (derived from Cyanobacteria)" ...
##  $ genus                                     : chr [1:1085] "AHJD-like viruses" "Abiotrophia" "Acanthamoeba" "Acaryochloris" ...
##  $ ShotgunWGS-ControlPig6GutMicrobiome-Day14 : num [1:1085] 29 5067 0 271 1988 ...
##  $ ShotgunWGS-ControlPig8GutMicrobiome-Day0  : num [1:1085] 0 5661 0 416 2981 ...
##  $ ShotgunWGS-ControlPig3GutMicrobiome-Day14 : num [1:1085] 153 4117 0 267 2071 ...
##  $ ShotgunWGS-TomatoPig14GutMicrobiome-Day7  : num [1:1085] 0 1576 1 131 1012 ...
##  $ ShotgunWGS-ControlPig5GutMicrobiome-Day7  : num [1:1085] 14 3708 0 230 1991 ...
##  $ ShotgunWGS-TomatoPig18GutMicrobiome-Day7  : num [1:1085] 1 1159 0 146 585 ...
##  $ ShotgunWGS-TomatoPig16GutMicrobiome-Day7  : num [1:1085] 2 2495 0 133 1538 ...
##  $ ShotgunWGS-ControlPig10GutMicrobiome-Day7 : num [1:1085] 0 1636 0 141 812 ...
##  $ ShotgunWGS-ControlPig2GutMicrobiome-Day0  : num [1:1085] 0 4534 0 338 2670 ...
##  $ ShotgunWGS-TomatoPig18GutMicrobiome-Day0  : num [1:1085] 0 2964 1 272 1665 ...
##  $ ShotgunWGS-ControlPig10GutMicrobiome-Day0 : num [1:1085] 0 3197 0 264 1411 ...
##  $ ShotgunWGS-ControlPig7GutMicrobiome-Day0  : num [1:1085] 0 2513 0 263 1652 ...
##  $ ShotgunWGS-ControlPig8GutMicrobiome-Day14 : num [1:1085] 342 4231 0 274 1795 ...
##  $ ShotgunWGS-TomatoPig11GutMicrobiome-Day0  : num [1:1085] 0 3101 0 237 2160 ...
##  $ ShotgunWGS-TomatoPig19GutMicrobiome-Day0  : num [1:1085] 0 3274 0 228 1729 ...
##  $ ShotgunWGS-TomatoPig17GutMicrobiome-Day14 : num [1:1085] 6 1424 0 83 683 ...
##  $ ShotgunWGS-ControlPig9GutMicrobiome-Day14 : num [1:1085] 131 3337 0 328 1722 ...
##  $ ShotgunWGS-ControlPig10GutMicrobiome-Day14: num [1:1085] 86 3383 0 238 1976 ...
##  $ ShotgunWGS-TomatoPig19GutMicrobiome-Day7  : num [1:1085] 0 1849 0 120 940 ...
##  $ ShotgunWGS-ControlPig5GutMicrobiome-Day14 : num [1:1085] 76 3864 0 363 2395 ...
##  $ ShotgunWGS-ControlPig2GutMicrobiome-Day7  : num [1:1085] 1 5590 0 306 4493 ...
##  $ ShotgunWGS-ControlPig6GutMicrobiome-Day7  : num [1:1085] 0 3120 0 201 1273 ...
##  $ ShotgunWGS-TomatoPig12GutMicrobiome-Day0  : num [1:1085] 0 2599 0 190 1451 ...
##  $ ShotgunWGS-TomatoPig14GutMicrobiome-Day0  : num [1:1085] 1 1453 0 70 846 ...
##  $ ShotgunWGS-ControlPig7GutMicrobiome-Day14 : num [1:1085] 67 2906 0 248 1870 ...
##  $ ShotgunWGS-TomatoPig11GutMicrobiome-Day14 : num [1:1085] 12 973 0 79 542 16 186 0 185 82 ...
##  $ ShotgunWGS-TomatoPig20GutMicrobiome-Day0  : num [1:1085] 0 3682 0 211 2232 ...
##  $ ShotgunWGS-ControlPig9GutMicrobiome-Day0  : num [1:1085] 2 2717 1 160 1547 ...
##  $ ShotgunWGS-TomatoPig11GutMicrobiome-Day7  : num [1:1085] 0 375 0 31 227 7 69 0 89 29 ...
##  $ ShotgunWGS-TomatoPig13GutMicrobiome-Day7  : num [1:1085] 0 2158 0 159 1774 ...
##  $ ShotgunWGS-TomatoPig17GutMicrobiome-Day0  : num [1:1085] 0 1409 0 197 762 ...
##  $ ShotgunWGS-TomatoPig19GutMicrobiome-Day14 : num [1:1085] 89 1059 0 81 580 ...
##  $ ShotgunWGS-TomatoPig13GutMicrobiome-Day0  : num [1:1085] 1 3634 0 207 2188 ...
##  $ ShotgunWGS-ControlPig2GutMicrobiome-Day14 : num [1:1085] 106 6111 0 386 3446 ...
##  $ ShotgunWGS-ControlPig1GutMicrobiome-Day7  : num [1:1085] 0 3815 1 190 1775 ...
##  $ ShotgunWGS-TomatoPig15GutMicrobiome-Day7  : num [1:1085] 1 1126 0 67 974 ...
##  $ ShotgunWGS-TomatoPig15GutMicrobiome-Day0  : num [1:1085] 0 3134 0 224 2207 ...
##  $ ShotgunWGS-TomatoPig12GutMicrobiome-Day7  : num [1:1085] 0 2376 0 144 1437 ...
##  $ ShotgunWGS-TomatoPig14GutMicrobiome-Day14 : num [1:1085] 0 1079 0 61 469 ...
##  $ ShotgunWGS-TomatoPig20GutMicrobiome-Day14 : num [1:1085] 47 926 0 61 486 18 205 0 193 41 ...
##  $ ShotgunWGS-ControlPig1GutMicrobiome-Day0  : num [1:1085] 0 5545 0 310 2638 ...
##  $ ShotgunWGS-ControlPig4GutMicrobiome-Day14 : num [1:1085] 102 3677 0 270 1919 ...
##  $ ShotgunWGS-ControlPig6GutMicrobiome-Day0  : num [1:1085] 4 2687 0 200 1640 ...
##  $ ShotgunWGS-TomatoPig16GutMicrobiome-Day0  : num [1:1085] 0 2959 0 176 1599 ...
##  $ ShotgunWGS-TomatoPig16GutMicrobiome-Day14 : num [1:1085] 3 973 0 98 609 34 222 0 255 149 ...
##  $ ShotgunWGS-TomatoPig18GutMicrobiome-Day14 : num [1:1085] 11 1075 0 86 446 ...
##  $ ShotgunWGS-ControlPig7GutMicrobiome-Day7  : num [1:1085] 0 1587 0 103 654 ...
##  $ ShotgunWGS-ControlPig4GutMicrobiome-Day7  : num [1:1085] 0 1709 0 165 1059 ...
##  $ ShotgunWGS-TomatoPig13GutMicrobiome-Day14 : num [1:1085] 0 1021 0 74 685 ...
##  $ ShotgunWGS-ControlPig8GutMicrobiome-Day7  : num [1:1085] 12 3035 0 259 1579 ...
##  $ ShotgunWGS-TomatoPig15GutMicrobiome-Day14 : num [1:1085] 17 1660 0 140 892 35 366 0 410 224 ...
##  $ ShotgunWGS-TomatoPig12GutMicrobiome-Day14 : num [1:1085] 19 2138 0 129 1334 ...
##  $ ShotgunWGS-TomatoPig20GutMicrobiome-Day7  : num [1:1085] 0 1699 0 121 645 ...
##  $ ShotgunWGS-ControlPig1GutMicrobiome-Day14 : num [1:1085] 14 3895 0 338 2158 ...
##  $ ShotgunWGS-ControlPig3GutMicrobiome-Day0  : num [1:1085] 0 4578 0 367 2356 ...
##  $ ShotgunWGS-ControlPig5GutMicrobiome-Day0  : num [1:1085] 0 4842 0 274 2894 ...
##  $ ShotgunWGS-ControlPig4GutMicrobiome-Day0  : num [1:1085] 0 4439 0 261 2733 ...
##  $ ShotgunWGS-ControlPig9GutMicrobiome-Day7  : num [1:1085] 0 703 0 54 384 6 159 0 200 45 ...
##  $ ShotgunWGS-ControlPig3GutMicrobiome-Day7  : num [1:1085] 6 4833 0 347 3158 ...
##  $ ShotgunWGS-TomatoPig17GutMicrobime-Day7   : num [1:1085] 0 1713 0 136 782 ...

Data filtering

Remove inplausible phyla

These phyla are not plausibly found in a rectal swab of a pig, and were incorrectly annotated, so we are removing them.

Genus.Counts.Filt <- Genus.AllSamples.Counts %>%
  filter(phylum != "Chordata" , phylum != "Arthropoda" , phylum != "Cnidaria" , 
         phylum != "Porifera" , phylum != "Echinodermata", phylum != "Streptophyta",
         phylum != "Platyhelminthes")

Transpose.

Genus.Counts.Filt.t <- as.tibble(t(Genus.Counts.Filt))
## Warning: `as.tibble()` was deprecated in tibble 2.0.0.
## Please use `as_tibble()` instead.
## The signature and semantics have changed, see `?as_tibble`.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.
## Warning: The `x` argument of `as_tibble.matrix()` must have unique column names if `.name_repair` is omitted as of tibble 2.0.0.
## Using compatibility `.name_repair`.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.
# make genus colnames
colnames(Genus.Counts.Filt.t) <- Genus.Counts.Filt.t[6,]

# remove domain, phylum, class, order, family
GenusOnly.Counts.Filt.t <- Genus.Counts.Filt.t[7:66,]

# convert character to numeric
GenusOnly.Counts.Filt.t <- as.data.frame(apply((GenusOnly.Counts.Filt.t), 2, as.numeric))

str(GenusOnly.Counts.Filt.t[,1:5])
## 'data.frame':    60 obs. of  5 variables:
##  $ AHJD-like viruses: num  29 0 153 0 14 1 2 0 0 0 ...
##  $ Abiotrophia      : num  5067 5661 4117 1576 3708 ...
##  $ Acanthamoeba     : num  0 0 0 1 0 0 0 0 0 1 ...
##  $ Acaryochloris    : num  271 416 267 131 230 146 133 141 338 272 ...
##  $ Acetivibrio      : num  1988 2981 2071 1012 1991 ...
# add back sample names as column
GenusOnly.Counts.Filt.t <- GenusOnly.Counts.Filt.t %>%
  mutate(Sample_Name = AllSamples.Metadata$Sample_Name)

# move Sample_Name to first column
GenusOnly.Counts.Filt.t <- GenusOnly.Counts.Filt.t %>%
  relocate(Sample_Name)

kable(head(GenusOnly.Counts.Filt.t))
Sample_Name AHJD-like viruses Abiotrophia Acanthamoeba Acaryochloris Acetivibrio Acetobacter Acetohalobium Acholeplasma Achromobacter Acidaminococcus Acidilobus Acidimicrobium Acidiphilium Acidithiobacillus Acidobacterium Acidothermus Acidovorax Aciduliprofundum Acinetobacter Actinobacillus Actinomyces Actinosynnema Aerococcus Aeromicrobium Aeromonas Aeropyrum Afipia Aggregatibacter Agrobacterium Ahrensia Ajellomyces Akkermansia Albidiferax Alcanivorax Algoriphagus Alicycliphilus Alicyclobacillus Aliivibrio Alistipes Alkalilimnicola Alkaliphilus Allochromatium Allomyces Alphabaculovirus Alphatorquevirus Alteromonas Aminobacterium Aminomonas Ammonifex Amycolatopsis Anabaena Anaerobaculum Anaerococcus Anaerofustis Anaeromyxobacter Anaerostipes Anaerotruncus Anaplasma Anoxybacillus Aquifex Arcanobacterium Archaeoglobus Arcobacter Aromatoleum Arthrobacter Arthroderma Arthrospira Ascovirus Asfivirus Aspergillus Asticcacaulis Atopobium Aurantimonas Aureococcus Avibacterium Avipoxvirus Azoarcus Azorhizobium Azospirillum Azotobacter Babesia Bacillus Bacteroides Bartonella Basfia Basidiobolus Batrachochytrium Bdellomicrovirus Bdellovibrio Beggiatoa Beijerinckia Bermanella Betabaculovirus Betaentomopoxvirus Betaretrovirus Beutenbergia Bicaudavirus Bifidobacterium Bigelowiella Blastocystis Blastopirellula Blattabacterium Blautia Bocavirus Boothiomyces Bordetella Borrelia Botryotinia Bpp-1-like viruses Brachybacterium Brachyspira Bradyrhizobium Brevibacillus Brevibacterium Brevundimonas Brucella Brugia Bryopsis Buchnera Bulleidia Burkholderia Butyrivibrio Caenorhabditis Cafeteria Caldanaerobacter Caldicellulosiruptor Calditerrivibrio Caldivirga Caminibacter Campylobacter Candida Candidatus Accumulibacter Candidatus Amoebophilus Candidatus Azobacteroides Candidatus Blochmannia Candidatus Carsonella Candidatus Cloacamonas Candidatus Desulforudis Candidatus Hamiltonella Candidatus Hodgkinia Candidatus Korarchaeum Candidatus Koribacter Candidatus Liberibacter Candidatus Pelagibacter Candidatus Phytoplasma Candidatus Protochlamydia Candidatus Puniceispirillum Candidatus Regiella Candidatus Riesia Candidatus Solibacter Candidatus Sulcia Candidatus Zinderia Capnocytophaga Carboxydothermus Cardiobacterium Carnobacterium Catenibacterium Catenulispora Catonella Caulobacter Cavemovirus Cellulomonas Cellulosilyticum Cellvibrio Cenarchaeum Chaetomium Chelativorans Chitinophaga Chlamydia Chlamydiamicrovirus Chlamydomonas Chlamydophila Chlorella Chloriridovirus Chlorobaculum Chlorobium Chloroflexus Chloroherpeton Chlorovirus Chondrus Chromera Chromobacterium Chromohalobacter Chryseobacterium Chrysodidymus Chthoniobacter Citreicella Citrobacter Citromicrobium Cladochytrium Clavibacter Clavispora Clostridium Coccidioides Coccolithovirus Coelomomyces Collimonas Collinsella Colwellia Comamonas Conexibacter Congregibacter Conidiobolus Coprinopsis Coprobacillus Coprococcus Coprothermobacter Coraliomargarita Corynebacterium Coxiella Crinivirus Croceibacter Crocosphaera Cronobacter Cryptobacterium Cryptomonas Cryptosporidium Cupriavidus Cyanidioschyzon Cyanidium Cyanobium Cyanophora Cyanothece Cylindrospermopsis Cylindrospermum Cyprinivirus Cytomegalovirus Cytophaga Debaryomyces Dechloromonas Deferribacter Dehalococcoides Dehalogenimonas Deinococcus Delftia Denitrovibrio Dependovirus Dermacoccus Desulfarculus Desulfatibacillum Desulfitobacterium Desulfobacterium Desulfococcus Desulfohalobium Desulfomicrobium Desulfonatronospira Desulfotalea Desulfotomaculum Desulfovibrio Desulfurispirillum Desulfurivibrio Desulfurococcus Desulfuromonas Dethiobacter Dethiosulfovibrio Dialister Dichelobacter Dickeya Dictyoglomus Dictyostelium Dinoroseobacter Dokdonia Dorea Durinskia Dyadobacter Ectocarpus Edwardsiella Eggerthella Ehrlichia Eikenella Eimeria Elusimicrobium Emericella Emiliania Encephalitozoon Endoriftia Enhydrobacter Entamoeba Enterobacter Enterococcus Enterocytozoon Entomophthora Epsilon15-like viruses Epulopiscium Eremococcus Eremothecium Erwinia Erysipelothrix Erythrobacter Escherichia Ethanoligenens Eubacterium Euglena Exiguobacterium Faecalibacterium Ferrimonas Ferroglobus Ferroplasma Fervidobacterium Fibrobacter Filifactor Filobasidiella Finegoldia Flavobacterium Floydiella Fluoribacter Francisella Frankia Fucus Fulvimarina Fusobacterium Gallionella Gammaretrovirus Gardnerella Gemella Gemmata Gemmatimonas Geobacillus Geobacter Geodermatophilus Giardia Gibberella Glaciecola Gloeobacter Gluconacetobacter Gluconobacter Gordonia Gracilaria Gracilariopsis Gramella Granulibacter Granulicatella Guillardia Haemophilus Hafnia Hahella Halalkalicoccus Halanaerobium Haliangium Haloarcula Halobacterium Haloferax Halogeometricum Halomicrobium Halomonas Haloquadratum Halorhabdus Halorhodospira Halorubrum Haloterrigena Halothermothrix Halothiobacillus Harpochytrium Helicobacter Helicosporidium Heliobacterium Hemiselmis Herbaspirillum Herminiimonas Herpetosiphon Heterosigma Hirschia Histophilus Hoeflea Holdemania Hyaloraphidium Hydrogenivirga Hydrogenobacter Hydrogenobaculum Hyperthermus Hyphomicrobium Hyphomonas Hypocrea Ichnovirus Idiomarina Ignicoccus Ignisphaera Ilyobacter Inovirus Intrasporangium Iridovirus Janibacter Jannaschia Janthinobacterium Jonesia Jonquetella Kangiella Ketogulonicigenium Kineococcus Kingella Klebsiella Kluyveromyces Kocuria Kordia Kosmotoga Kribbella Kryptoperidinium Ktedonobacter Kytococcus L5-like viruses LUZ24-like viruses Labrenzia Laccaria Lachancea Lachnum Lactobacillus Lactococcus Lambda-like viruses Laminaria Laribacter Lawsonia Leadbetterella Leeuwenhoekiella Legionella Leifsonia Leishmania Lentisphaera Leotia Leptosira Leptospira Leptospirillum Leptothrix Leptotrichia Leuconostoc Limnobacter Listeria Listonella Loa Lodderomyces Loktanella Lutiella Lymphocryptovirus Lymphocystivirus Lyngbya Lysinibacillus Macavirus Macrococcus Magnaporthe Magnetococcus Magnetospirillum Malassezia Malawimonas Mannheimia Maribacter Maricaulis Marinobacter Marinococcus Marinomonas Mariprofundus Maritimibacter Marivirga Megasphaera Meiothermus Mesoplasma Mesorhizobium Metallosphaera Methanobrevibacter Methanocaldococcus Methanocella Methanococcoides Methanococcus Methanocorpusculum Methanoculleus Methanohalobium Methanohalophilus Methanoplanus Methanopyrus Methanoregula Methanosaeta Methanosarcina Methanosphaera Methanosphaerula Methanospirillum Methanothermobacter Methanothermococcus Methanothermus Methylacidiphilum Methylibium Methylobacillus Methylobacter Methylobacterium Methylocella Methylococcus Methylophaga Methylosinus Methylotenera Methylovorus Meyerozyma Micrococcus Microcoleus Microcystis Micromonas Micromonospora Microscilla Mitsuokella Mobiluncus Molluscipoxvirus Moniliophthora Monomastix Monosiga Moorella Moraxella Moritella Mu-like viruses Mucilaginibacter Mycobacterium Mycoplasma Myxococcus N15-like viruses N4-like viruses Naegleria Nakamurella Nakaseomyces Nanoarchaeum Natranaerobius Natrialba Natronomonas Nautilia Nectria Neisseria Neolecta Neorickettsia Neosartorya Nephroselmis Neptuniibacter Neurospora Nitratiruptor Nitrobacter Nitrococcus Nitrosococcus Nitrosomonas Nitrosopumilus Nitrosospira Nitrospira Nocardia Nocardioides Nocardiopsis Nodularia Nosema Nostoc Novosphingobium Oceanibulbus Oceanicaulis Oceanicola Oceanithermus Oceanobacillus Ochrobactrum Ochromonas Octadecabacter Odontella Oedogonium Oenococcus Oligotropha Olsenella Oltmannsiellopsis Opitutus Oribacterium Orientia Ornithobacterium Orthopoxvirus Oscillatoria Oscillochloris Ostreavirus Ostreococcus Oxalobacter P1-like viruses P2-like viruses P22-like viruses Paenibacillus Paludibacter Pantoea Parabacteroides Parachlamydia Parachlorella Paracoccidioides Paracoccus Paraglomus Paramecium Parascardovia Parvibaculum Parvularcula Pasteurella Paulinella Paxillus Pectobacterium Pediococcus Pedobacter Pelagibaca Pelobacter Pelodictyon Pelotomaculum Penicillium Peptoniphilus Peptostreptococcus Perkinsus Persephonella Petrotoga Phaeobacter Phaeodactylum Phaeosphaeria Phaeovirus Phenylobacterium Phi29-like viruses PhiC31-like viruses Phieco32-like viruses Photobacterium Photorhabdus Physoderma Phytophthora Pichia Picrophilus Pirellula Planctomyces Planococcus Plasmodium Plectrovirus Plesiocystis Podospora Polaribacter Polaromonas Polychytrium Polynucleobacter Polysphondylium Porphyra Porphyromonas Postia Prasinovirus Prevotella Prochlorococcus Propionibacterium Prosthecochloris Proteromonas Proteus Prototheca Providencia Pseudendoclonium Pseudoalteromonas Pseudomonas Pseudoramibacter Pseudovibrio Psychrobacter Psychroflexus Psychromonas Pycnococcus Pylaiella Pyramidobacter Pyramimonas Pyrenophora Pyrobaculum Pyrococcus Pythium Ralstonia Ranavirus Raphidiopsis Reclinomonas Reinekea Renibacterium Rhadinovirus Rhizobium Rhodobacter Rhodococcus Rhodomicrobium Rhodomonas Rhodopirellula Rhodopseudomonas Rhodospirillum Rhodothermus Rhopalomyces Rickettsia Rickettsiella Riemerella Robiginitalea Roseburia Roseibium Roseiflexus Roseobacter Roseomonas Roseovarius Rothia Rozella Rubrobacter Rudivirus Ruegeria Ruminococcus SP6-like viruses SPO1-like viruses SPbeta-like viruses Saccharomonospora Saccharomyces Saccharophagus Saccharopolyspora Saccoglossus Sagittula Salinibacter Salinispora Salmonella Sanguibacter Saprolegnia Scardovia Scenedesmus Scheffersomyces Schizophyllum Schizosaccharomyces Sclerotinia Sebaldella Segniliparus Selenomonas Serratia Shewanella Shigella Shuttleworthia Sideroxydans Simonsiella Simplexvirus Sinorhizobium Slackia Sodalis Sorangium Sphaerobacter Sphingobacterium Sphingobium Sphingomonas Sphingopyxis Spirochaeta Spiromicrovirus Spiroplasma Spirosoma Sporosarcina Stackebrandtia Staphylococcus Staphylothermus Starkeya Stenotrophomonas Stigeoclonium Stigmatella Streptobacillus Streptococcus Streptomyces Streptosporangium Subdoligranulum Sulfitobacter Sulfolobus Sulfuricurvum Sulfurihydrogenibium Sulfurimonas Sulfurospirillum Sulfurovum Symbiobacterium Synchytrium Synechococcus Synechocystis Synedra Syntrophobacter Syntrophomonas Syntrophothermus Syntrophus T1-like viruses T4-like viruses T5-like viruses T7-like viruses Talaromyces Tectivirus Teredinibacter Terriglobus Tetragenococcus Tetrahymena Thalassiosira Thalassobium Thauera Theileria Thermaerobacter Thermanaerovibrio Thermincola Thermoanaerobacter Thermoanaerobacterium Thermobaculum Thermobifida Thermobispora Thermococcus Thermocrinis Thermodesulfovibrio Thermofilum Thermomicrobium Thermomonospora Thermoplasma Thermoproteus Thermosediminibacter Thermosinus Thermosipho Thermosphaera Thermosynechococcus Thermotoga Thermus Thioalkalivibrio Thiobacillus Thiomicrospira Thiomonas Tolumonas Toxoplasma Treponema Trichodesmium Trichomonas Trichophyton Trichoplax Tropheryma Truepera Trypanosoma Tsukamurella Tuber Turicibacter Uncinocarpus Ureaplasma Ustilago VP2-like phages Vanderwaltozyma Varicellovirus Variovorax Vaucheria Veillonella Verminephrobacter Verrucomicrobium Verticillium Vibrio Victivallis Volvox Vulcanisaeta Waddlia Weissella Whispovirus Wigglesworthia Wolbachia Wolinella Xanthobacter Xanthomonas Xenorhabdus Xylanimonas Xylella Yarrowia Yatapoxvirus Yersinia Zunongwangia Zygosaccharomyces Zymomonas c2-like viruses phiKMV-like viruses phiKZ-like viruses unclassified (derived from Actinobacteria (class)) unclassified (derived from Alicyclobacillaceae) unclassified (derived from Alloherpesviridae) unclassified (derived from Alphaproteobacteria) unclassified (derived from Alteromonadales) unclassified (derived from Bacteria) unclassified (derived from Bacteroidetes) unclassified (derived from Betaproteobacteria) unclassified (derived from Burkholderiales) unclassified (derived from Campylobacterales) unclassified (derived from Candidatus Poribacteria) unclassified (derived from Caudovirales) unclassified (derived from Chromerida) unclassified (derived from Chroococcales) unclassified (derived from Clostridiales Family XI. Incertae Sedis) unclassified (derived from Clostridiales) unclassified (derived from Deltaproteobacteria) unclassified (derived from Elusimicrobia) unclassified (derived from Erysipelotrichaceae) unclassified (derived from Euryarchaeota) unclassified (derived from Flavobacteria) unclassified (derived from Flavobacteriaceae) unclassified (derived from Flavobacteriales) unclassified (derived from Fuselloviridae) unclassified (derived from Gammaproteobacteria) unclassified (derived from Lachnospiraceae) unclassified (derived from Marseillevirus family) unclassified (derived from Methylophilales) unclassified (derived from Mononegavirales) unclassified (derived from Myoviridae) unclassified (derived from Opitutaceae) unclassified (derived from Pelagophyceae) unclassified (derived from Phycodnaviridae) unclassified (derived from Podoviridae) unclassified (derived from Poxviridae) unclassified (derived from Proteobacteria) unclassified (derived from Rhodobacteraceae) unclassified (derived from Rhodobacterales) unclassified (derived from Rickettsiales) unclassified (derived from Ruminococcaceae) unclassified (derived from Siphoviridae) unclassified (derived from Thermotogales) unclassified (derived from Thiotrichales) unclassified (derived from Verrucomicrobia subdivision 3) unclassified (derived from Verrucomicrobiales) unclassified (derived from Vibrionaceae) unclassified (derived from Vibrionales) unclassified (derived from Viruses) unclassified (derived from other sequences)
ShotgunWGS-ControlPig6GutMicrobiome-Day14 29 5067 0 271 1988 66 1036 779 192 50181 10 59 244 245 365 389 790 217 794 2621 1004 193 341 25 851 31 23 368 517 16 11 1474 342 255 171 81 1438 520 1983 413 13144 198 0 0 8 148 509 101 915 161 473 130 4600 1549 1311 4289 6643 22 1391 554 531 461 513 412 951 14 92 0 0 167 287 35624 112 0 0 0 286 190 165 192 8 27258 318931 222 744 0 0 1 334 81 96 27 0 0 0 406 0 40993 0 0 295 52 17707 0 0 947 291 19 46 301 5181 712 1770 279 126 276 35 1 86 2321 2463 40314 135 0 6815 8872 295 28 33 4599 42 203 196 702 51 0 63 1459 48 0 75 671 23 61 169 177 46 18 7 1612 14 0 2284 4439 72 232 4894 250 177 815 0 291 2656 581 15 30 274 2203 137 1 127 135 0 1 662 2359 907 468 4 0 0 402 350 328 0 85 13 456 16 0 408 17 297762 12 0 0 0 19115 257 119 324 143 0 26 1405 24605 336 203 6812 194 0 475 245 371 3943 1 26 847 8 25 12 5 1558 55 0 0 0 1635 32 358 555 3574 330 1030 221 644 0 31 309 591 14579 526 613 264 575 70 755 11294 4843 331 226 17 644 426 992 24138 330 453 1217 175 136 366 20388 0 1864 0 296 10647 64 28 0 386 54 0 2 13 32 99 615 10711 47 0 4 540 116 36 282 287 333 1977 17513 276925 3 2132 134155 132 126 95 785 4768 594 73 2960 4955 0 0 419 950 0 40 7250 131 1 2547 156 92 211 7035 5443 207 24 96 1 470 112 247 125 2 0 1415 162 478 28 1195 0 433 43 915 283 87 64 52 61 39 121 59 58 226 41 76 2226 98 0 2189 0 5259 1 147 222 519 4 122 474 31 13306 0 69 142 263 43 78 167 0 2 288 43 26 2406 0 149 0 111 128 225 334 271 109 68 359 34 785 38 261 115 371 216 0 140 162 1 0 61 13 29 0 178641 2714 65 0 145 335 1469 1324 556 216 85 102 0 2 516 1 217 2436 1674 31 4368 0 8 13 42 81 0 0 72 1157 0 503 58 579 640 26 0 381 784 137 482 0 565 47 88 536 39571 549 194 308 43 1912 417 179 400 1200 1296 383 128 136 276 105 284 269 1565 499 213 423 350 10 45 102 181 204 40 588 198 374 13 24 141 173 24 220 73 344 48 219 309 106534 5991 0 15 0 57 4305 92 50 0 657 1711 1430 576 0 2 32 220 71 4 1577 32 62 158 24 899 0 55 155 1 38 96 391 324 121 471 423 41 256 191 365 426 178 69 0 689 271 21 98 114 158 1728 274 0 53 8 1 776 144 25686 0 955 19432 45 0 0 42 79 0 92 514 0 1 0 7823 4818 526 24663 32 0 4 337 0 42 846 257 85 410 5 0 694 1736 3321 25 2655 1255 4478 50 1637 1371 32 222 677 20 36 39 0 188 0 0 0 797 335 0 63 8 57 158 375 1 138 0 118 17 901 425 0 192 0 21 8873 5 1 1029670 660 872 145 0 331 0 118 0 769 2976 5626 50 505 190 502 0 0 1711 0 18 97 443 0 581 0 25 6 203 141 0 814 604 739 83 4 603 937 533 648 0 215 9 491 732 93165 17 1503 364 38 136 332 0 817 0 298 145083 5 35 11 133 54 684 421 26 30 456 399 771 550 1 639 0 49 34 57 14 2577 34 36152 519 2720 237 5045 211 50 1 550 13349 101 434 540 443 82 290 263 2362 2 0 2579 0 178 4604 30 98 247 0 128 681 110465 1855 255 82351 48 158 140 608 359 274 232 3519 0 2287 497 0 833 3600 1217 980 0 22 0 0 46 0 172 226 11 34 61 21 165 16 946 739 2853 6464 3550 554 444 223 490 95 359 85 250 198 182 6 1924 2471 905 14 463 2161 570 311 295 238 99 350 43 3169 408 458 1 68 57 174 46 140 8 774 9 192 48 0 11 0 183 1 23909 272 65 7 2167 663 98 17 71 174 0 10 114 430 237 760 138 674 247 52 0 1199 1535 9 199 2 0 4 103 1173 0 56 32 580 946 11 365 48 26 64 0 33 421 12636 186 246 17626 340 602 668 261 0 409 16441 0 11 0 206 86 2 0 7 0 3 39 39 16 15985 768 115 0 120 119 56 33 252 48
ShotgunWGS-ControlPig8GutMicrobiome-Day0 0 5661 0 416 2981 86 1373 1269 298 39909 20 126 344 375 539 554 1502 498 1045 2461 1433 281 354 68 1124 33 21 416 811 45 12 2182 633 333 624 149 1835 704 5891 337 19539 220 0 1 18 287 1036 186 1199 221 741 249 6175 2355 1943 5468 13087 48 1884 674 974 540 708 525 1501 18 126 1 0 145 327 42713 149 2 0 0 470 250 295 300 14 36494 442199 338 862 0 0 4 444 114 164 58 1 0 0 601 1 95976 1 5 514 166 23637 1 0 1069 509 40 14 427 6708 1036 2244 417 171 394 47 0 96 2327 3547 39907 204 0 9162 11870 450 53 66 8514 74 256 377 1730 69 2 160 1952 57 0 97 899 18 79 254 284 70 24 1 1865 49 0 3838 5382 89 338 7907 369 272 953 0 413 4072 586 26 17 457 3454 128 18 150 218 6 0 1106 2794 1107 649 14 0 0 599 500 680 1 375 47 688 32 0 616 25 424056 55 0 0 1 20485 391 250 412 169 0 43 2230 26769 543 473 3838 228 0 877 240 470 5494 1 74 1257 9 18 383 14 2011 56 1 1 0 2596 35 927 588 3698 379 1387 349 956 0 56 436 981 20232 744 911 396 762 124 1047 13009 9335 416 395 35 773 699 1632 10396 320 569 1554 375 193 776 62948 0 2525 0 395 12983 103 56 0 678 62 4 11 42 67 1267 763 15015 76 0 9 897 230 42 375 436 451 4404 24857 252845 0 2864 124365 205 108 114 968 6963 1080 117 3329 7259 0 1 750 1431 0 39 10721 196 1 5115 243 179 340 8831 7012 275 59 174 2 676 221 274 247 3 0 2455 225 617 31 1566 0 567 61 1200 387 114 90 60 96 66 172 109 90 308 50 94 2722 156 0 3541 0 5937 0 181 349 658 1 117 726 45 18363 0 96 169 325 46 133 255 0 0 486 60 24 2995 1 149 3 223 202 371 475 374 162 121 625 69 936 72 291 302 644 346 0 242 240 0 1 127 42 33 0 153576 2761 104 0 193 595 1719 1775 680 404 128 272 0 1 809 1 418 3048 1911 74 5766 0 14 25 80 108 0 0 113 1562 0 601 91 773 826 35 0 387 1442 258 703 0 736 85 132 1023 25118 864 266 523 57 2802 696 221 564 1776 1748 528 155 232 454 176 350 401 2403 614 222 509 539 27 98 207 473 354 84 872 172 534 49 94 231 160 32 285 142 410 74 331 673 91970 10473 0 23 0 88 5010 111 80 4 992 2469 1700 910 0 15 112 326 81 5 2109 73 99 221 35 1301 0 63 181 0 75 97 503 459 178 765 643 54 349 307 462 851 276 83 1 952 391 30 135 159 243 2209 488 0 69 3 0 1108 102 30551 4 1555 7440 81 0 0 95 143 0 161 741 2 16 19 11386 8899 601 39080 47 0 7 338 0 71 1019 366 141 528 15 0 871 2258 5681 35 3491 1261 5463 62 2644 2008 43 276 995 26 83 54 0 173 8 0 0 1130 481 0 156 23 109 290 666 1 236 0 162 40 1645 1012 0 395 0 23 16492 7 4 820657 1108 1128 263 0 427 1 240 0 1015 3925 2424 74 754 328 705 0 1 3061 0 26 134 626 0 761 0 24 2 349 204 1 1049 795 991 151 3 983 1365 772 972 1 306 10 840 1135 72078 39 2141 567 106 243 397 0 1032 0 532 233438 0 42 11 204 108 842 638 18 53 667 550 1238 699 1 855 5 87 40 164 12 3363 68 33861 680 3781 510 4244 176 92 0 907 17333 168 566 856 1017 188 368 326 4106 7 1 3749 0 274 5633 58 178 421 3 206 1115 32512 2943 509 65446 102 212 206 584 432 343 417 4358 0 4236 637 0 1217 4845 1665 1431 0 85 1 35 27 1 249 414 19 56 98 29 243 19 1323 951 3534 8290 4124 739 580 338 814 130 489 121 388 323 300 7 2772 5000 1238 12 599 3069 812 445 448 358 133 480 41 5331 516 1577 7 68 90 280 121 147 27 1135 23 241 98 0 23 0 323 1 19797 508 303 24 3077 1561 166 30 152 212 0 15 200 646 319 1179 169 796 327 109 0 1352 1959 27 269 3 0 6 209 1414 0 141 44 821 2204 17 815 72 49 171 0 47 520 16819 355 329 28383 550 1064 881 725 0 739 21035 0 16 0 587 597 2 2 33 0 2 85 95 12 51452 1069 191 0 336 254 80 33 311 33
ShotgunWGS-ControlPig3GutMicrobiome-Day14 153 4117 0 267 2071 60 1015 817 197 31994 25 81 243 222 309 301 1126 245 653 1798 698 181 737 18 762 26 12 289 443 13 11 1419 464 302 272 141 1369 486 2917 325 13386 173 0 0 5 195 607 112 836 195 485 147 4189 1512 1216 4240 8660 44 1416 469 484 407 569 384 778 14 89 0 1 111 213 19924 84 3 0 0 338 184 238 153 6 26183 294856 170 561 0 0 0 315 50 96 38 0 2 0 222 0 25359 0 1 285 90 18590 0 0 841 246 21 12 223 4840 659 1647 205 115 225 30 1 76 1531 2412 34373 104 0 6622 9318 278 21 37 4769 55 210 216 790 31 1 87 1519 39 0 52 694 10 80 150 183 65 11 2 1413 31 0 2428 4140 69 270 3721 208 246 804 0 203 2758 492 21 31 309 2284 75 0 73 163 3 1 689 1986 898 610 7 0 0 374 336 337 0 168 31 470 27 0 335 39 299010 18 0 0 0 20465 323 169 293 117 0 14 1264 24714 356 247 1818 145 0 435 188 322 3273 0 29 873 6 16 462 10 1563 47 0 1 0 1595 15 369 381 3124 313 966 246 760 0 28 299 604 14708 498 585 338 528 71 822 10274 5420 271 237 22 589 492 895 7351 244 440 1109 153 118 414 32261 2 1656 2 238 9588 59 36 0 452 39 4 2 14 53 113 478 13222 54 0 4 584 103 33 305 209 309 3066 19759 269071 0 2082 175626 142 80 59 794 4985 621 74 2373 4924 0 1 509 954 0 26 6742 161 2 1795 172 80 241 6468 5172 177 24 104 0 536 137 170 103 5 0 1371 176 656 25 1008 0 369 44 910 274 86 46 37 57 35 103 78 41 202 49 75 2050 107 0 2163 0 5150 0 206 270 522 0 98 571 27 12582 0 88 99 182 35 76 198 0 0 326 35 16 2107 1 96 2 117 143 296 243 211 105 83 270 44 600 35 155 167 372 185 1 125 122 1 1 61 14 16 0 163871 4344 49 0 160 372 1507 1139 484 187 61 154 0 1 531 3 269 2470 2143 61 4387 0 12 17 62 63 0 0 94 1202 0 520 65 531 494 20 0 270 763 151 470 0 463 60 112 542 27391 521 194 311 34 1931 418 146 445 1226 1154 430 103 117 321 121 247 268 1527 380 171 373 343 38 54 138 284 228 61 631 162 376 32 77 136 106 23 177 73 376 56 222 348 57206 1189 0 12 1 55 3974 79 40 0 441 1460 1180 576 0 2 37 192 58 4 1539 51 68 159 21 814 0 32 113 1 36 48 297 355 126 482 406 38 241 212 272 397 158 63 0 701 366 20 68 114 153 1575 220 0 52 7 0 876 74 13557 2 1166 7969 78 0 0 72 96 0 112 582 7 3 1 8001 5183 404 23919 22 0 3 203 0 40 369 258 114 274 30 0 609 1780 3350 21 2774 1015 4192 33 1488 1213 19 219 660 18 49 22 0 181 1 1 0 850 288 0 55 13 83 229 397 2 111 0 98 20 906 780 0 271 0 32 8026 5 1 890023 964 618 169 0 274 2 147 0 709 2616 2232 40 465 169 520 0 0 1543 0 9 119 418 0 576 1 20 2 210 95 0 684 577 650 88 7 579 910 592 631 0 223 7 453 803 83235 24 1635 348 62 126 341 0 697 0 377 131810 5 33 13 112 72 871 333 16 28 464 379 760 391 1 376 0 33 31 62 13 2214 42 37426 513 2671 415 3675 139 113 0 586 11188 122 423 579 515 108 317 249 2292 0 0 2461 0 141 4510 51 99 215 1 136 823 431655 1787 294 75804 76 111 141 500 353 258 266 3115 0 3911 496 0 750 3692 1165 910 0 36 0 20 9 0 153 247 24 32 73 13 162 30 964 633 2661 5809 2962 484 355 177 532 85 348 93 304 218 175 8 1960 3508 917 25 474 2175 514 269 324 250 114 285 14 3013 358 466 0 31 40 167 65 93 19 641 10 152 28 0 9 0 292 0 17258 319 140 13 2111 620 109 17 111 219 0 15 129 368 241 770 129 447 213 42 0 1067 1432 12 246 4 0 7 104 1054 0 81 29 577 818 5 672 41 21 63 0 37 319 11368 207 247 19120 311 604 592 345 0 517 16846 0 16 0 214 163 1 0 19 3 0 63 48 9 21377 1145 134 0 159 138 34 16 182 62
ShotgunWGS-TomatoPig14GutMicrobiome-Day7 0 1576 1 131 1012 41 399 391 90 11378 7 27 100 102 135 113 335 195 442 1000 405 91 135 8 1263 10 15 263 230 13 6 578 156 183 104 35 555 391 1410 132 5297 110 0 0 24 197 242 55 278 52 225 88 1576 762 457 1944 3531 18 532 170 443 217 243 155 406 4 44 0 0 43 62 3501 38 0 0 0 159 67 79 108 3 10048 107303 79 331 0 0 2 148 50 37 61 0 0 0 203 1 5542 0 1 105 40 7102 0 0 298 213 29 4 171 1844 240 599 121 46 160 54 0 119 694 1041 10853 66 0 2529 3572 122 7 21 2070 21 84 84 352 32 0 36 529 43 0 36 240 9 32 97 59 21 34 6 425 11 1 846 1489 41 101 1376 104 84 225 1 129 1136 192 15 6 89 759 28 0 36 50 1 1 255 737 308 216 4 2 0 178 173 137 0 33 12 410 5 0 205 7 109452 9 0 0 6 3136 214 62 113 55 0 14 471 5653 142 82 3171 101 0 184 96 268 921 0 19 320 3 5 13 3 580 23 0 0 0 571 6 193 163 1136 156 371 81 258 0 24 135 286 5163 200 254 138 207 39 304 3462 3867 114 112 13 240 219 392 1396 110 289 424 94 59 217 8251 0 560 0 257 2816 38 23 0 247 28 5 6 13 42 48 448 4365 17 6 3 290 71 17 210 122 111 12096 7325 55035 1 693 47886 196 35 44 255 1750 305 34 1326 1745 2 0 285 330 0 11 2992 65 0 511 118 31 61 2475 1952 95 7 41 1 176 66 58 69 2 0 622 80 190 9 849 0 217 24 304 134 42 26 23 28 14 68 29 32 110 22 35 731 78 0 3519 0 1694 1 93 122 200 0 33 357 22 4418 0 45 57 75 18 25 68 0 3 301 29 12 713 1 52 0 76 59 138 136 158 80 29 226 26 610 19 140 103 183 106 0 83 90 0 0 60 10 8 0 104615 809 141 0 90 240 435 510 259 104 37 85 0 1 237 0 106 745 545 41 1651 0 18 6 25 36 0 0 24 466 0 206 29 211 225 16 0 187 405 67 298 0 278 36 36 235 6151 229 129 110 23 2746 273 87 179 537 481 153 54 81 122 79 120 124 765 196 90 166 361 23 58 64 98 107 44 234 48 182 28 22 83 68 10 70 27 116 28 84 153 7754 5070 0 5 0 46 1363 58 136 4 222 718 595 234 0 2 27 97 39 6 599 13 21 71 12 511 0 19 55 2 45 30 131 140 49 256 239 22 116 92 141 219 67 24 1 257 80 12 31 38 72 598 94 0 26 4 0 286 38 2330 1 295 3460 22 0 0 44 29 0 54 378 126 6 5 2964 1842 290 10529 19 0 1 78 0 9 77 108 51 305 7 0 369 615 1243 10 949 303 1301 12 900 614 17 68 305 13 32 16 0 53 1 0 0 686 290 0 38 8 50 54 151 4 127 0 57 6 316 210 0 100 0 9 3428 1 0 277930 276 501 54 0 292 0 159 0 652 1471 630 19 263 79 476 0 1 645 0 13 53 217 0 240 0 7 4 113 69 1 283 189 298 45 1 199 438 285 247 0 113 20 175 318 12198 4 557 189 22 54 145 0 259 0 147 58127 1 15 3 76 41 271 140 5 11 167 157 1022 198 0 81 0 20 11 47 6 823 18 11115 335 1912 1500 1089 63 54 0 223 3372 106 122 269 210 62 120 76 1179 0 0 809 0 73 3212 22 57 151 0 46 272 9055 785 139 25259 27 84 61 190 120 112 118 1170 2 851 164 0 335 1360 410 352 0 18 0 5 9 1 104 80 9 15 29 6 66 11 357 290 978 2398 1040 196 194 87 242 42 149 33 111 110 101 3 819 1484 357 5 158 805 235 163 116 146 64 670 8 2161 173 251 2 19 26 60 19 48 6 406 8 80 23 0 11 0 67 0 4735 84 45 7 1727 407 59 9 67 73 0 17 74 260 86 363 126 207 121 30 0 772 501 4 63 2 0 6 64 377 0 32 77 929 413 10 378 19 17 59 0 17 208 4424 93 112 5413 194 284 203 233 0 357 5529 0 13 0 83 60 1 1 3 0 1 41 80 5 11160 395 93 1 55 47 106 31 88 115
ShotgunWGS-ControlPig5GutMicrobiome-Day7 14 3708 0 230 1991 64 876 787 277 29680 16 90 205 243 341 304 1189 445 896 1778 623 179 224 47 1684 27 31 383 494 30 19 1214 514 341 302 152 1170 647 3503 270 12270 205 0 0 7 334 632 105 815 168 448 139 3168 1475 1134 3828 8846 43 1142 438 523 438 460 416 735 5 88 0 0 87 125 12791 99 1 0 0 406 184 220 225 6 22326 225359 206 673 0 0 2 294 67 101 64 0 0 0 218 1 66063 0 2 300 96 16872 2 0 830 262 24 6 203 4201 669 1387 214 105 258 36 0 202 1097 2653 28995 112 0 5764 8126 244 30 41 5906 36 308 231 808 57 1 83 1241 57 0 66 569 26 72 175 177 53 27 5 922 31 1 1881 3472 77 197 3799 193 178 546 0 191 2805 333 24 16 244 1743 86 4 107 113 4 0 656 1789 706 438 9 0 0 398 364 297 0 230 31 470 29 0 310 8 272328 31 0 0 0 10233 357 194 297 150 0 19 1189 18751 299 302 1826 152 0 433 150 340 2680 0 42 957 6 16 600 4 1285 41 0 0 0 1338 14 823 372 2490 226 825 329 550 0 35 279 608 13231 422 516 261 445 56 673 8336 7391 273 208 24 478 521 940 12959 244 480 897 154 122 367 32973 1 1235 0 416 7772 62 48 0 450 45 3 4 37 50 220 505 9131 76 0 6 539 130 39 297 223 296 1661 16498 181032 2 1686 114740 270 78 94 626 4040 672 49 2351 3360 0 1 530 890 0 36 6669 182 2 3518 173 102 211 5448 4373 164 36 76 0 372 133 153 115 0 0 1229 144 430 15 1322 0 462 35 747 277 86 82 42 55 39 132 51 80 211 40 69 1732 111 0 1893 1 4174 0 240 301 407 1 106 601 35 10907 0 68 100 146 41 64 188 0 0 475 42 24 1924 0 96 0 135 135 347 225 243 191 74 314 61 633 23 130 154 353 168 1 142 126 2 0 72 19 17 0 549401 1876 81 1 188 455 975 1002 548 173 63 174 0 2 485 0 297 1940 1203 95 3963 0 13 24 51 97 0 0 67 946 0 422 47 422 597 18 0 266 725 127 619 0 566 75 97 488 16036 481 172 330 50 1992 533 211 340 1187 1102 361 95 137 298 118 257 294 1574 339 143 371 397 25 81 130 370 302 73 615 141 399 60 105 196 147 13 120 68 256 57 216 317 50915 1108 0 7 0 56 3533 107 167 0 500 1488 1015 493 0 6 45 159 61 4 1383 55 75 151 18 918 0 33 95 0 90 49 296 316 116 503 465 31 295 193 207 413 162 62 0 613 290 20 76 111 143 1336 219 0 60 4 0 693 91 9197 2 858 4884 34 0 0 73 88 0 112 878 2 3 2 7065 3905 494 22016 30 0 6 226 0 39 694 228 100 545 28 1 690 1178 2743 34 2142 804 3400 28 1528 1306 36 172 604 16 47 30 0 103 3 1 0 1070 491 1 80 12 102 245 435 0 87 0 95 12 804 838 0 363 0 19 7825 6 2 507229 900 659 148 0 430 0 253 1 949 2830 1596 53 459 151 730 0 0 1687 0 9 99 427 1 704 0 21 2 211 114 3 650 672 617 82 3 592 1020 585 579 0 214 29 406 633 47030 34 1358 396 87 159 214 0 588 0 395 129723 4 39 2 112 71 575 351 8 34 379 346 837 346 0 570 1 46 26 59 15 2082 51 20968 575 3220 209 2773 178 68 0 606 9232 174 340 582 541 159 304 232 2377 3 0 1727 0 163 4225 54 110 266 2 139 645 41068 1825 269 43518 57 125 117 408 296 260 286 2779 0 4149 393 1 770 2980 979 907 0 42 1 7 21 0 171 184 6 24 59 19 255 14 840 612 2151 5295 2641 400 326 209 537 86 290 82 250 185 217 11 1748 2666 905 5 334 1910 481 348 324 280 111 922 22 3432 335 467 4 27 59 165 46 104 14 815 13 160 37 0 18 0 322 1 13193 336 182 4 2870 1165 99 22 99 119 0 23 145 296 253 780 193 359 296 52 0 1077 964 17 192 6 0 12 133 989 1 124 113 840 1026 30 600 43 22 102 0 26 369 10621 190 215 13467 387 493 425 349 0 607 15029 0 32 0 173 190 0 1 6 0 3 74 95 10 24441 718 129 0 197 139 122 41 200 13
ShotgunWGS-TomatoPig18GutMicrobiome-Day7 1 1159 0 146 585 33 265 338 195 10604 6 85 139 135 171 261 1161 145 368 634 527 172 100 49 356 12 13 115 267 17 9 717 512 104 187 138 488 184 1636 139 4443 90 0 0 92 74 196 34 225 148 229 53 1402 450 593 1437 2878 27 467 183 244 148 194 183 713 2 37 0 0 79 104 15164 47 0 0 0 197 110 105 84 5 8839 168421 67 214 0 0 0 146 26 73 20 0 0 0 203 0 104397 0 0 176 45 7266 2 0 447 134 17 13 166 2073 462 502 216 102 134 49 0 32 378 1349 10996 58 1 2222 3243 109 14 11 1242 10 129 87 418 19 1 25 429 8 0 25 352 7 43 48 69 27 4 0 773 10 0 1136 1109 20 57 1120 178 58 374 0 201 751 223 11 4 170 1247 39 2 64 51 0 0 333 1052 372 250 16 0 0 173 132 203 0 168 13 459 19 0 397 15 101208 12 0 0 0 15652 149 183 150 67 0 12 389 5587 101 172 1853 73 0 204 70 189 1767 0 10 594 1 6 704 5 614 20 0 0 0 789 11 270 104 1041 230 443 248 232 0 37 106 209 4918 195 238 109 174 33 299 2951 1806 91 115 14 217 103 314 3230 101 198 423 62 80 220 8626 0 796 0 129 4429 30 20 0 155 28 0 1 6 14 31 352 4166 30 0 0 170 41 28 160 64 214 16141 8612 66371 0 677 136627 60 35 31 245 2071 169 40 881 2888 0 0 223 766 0 18 2416 66 5 4843 44 91 208 2135 2027 160 4 70 0 231 73 106 92 1 0 678 134 149 6 431 0 169 14 224 144 54 20 14 20 21 60 26 22 143 14 39 643 68 0 927 0 1360 0 173 142 244 0 40 183 15 4918 0 19 26 71 14 84 99 0 0 122 9 6 638 1 130 0 167 88 197 238 103 54 44 326 9 543 23 114 86 142 175 0 62 107 2 1 45 5 16 0 185934 932 255 0 74 181 612 835 237 191 31 51 0 0 235 2 335 796 670 72 1640 2 27 11 42 30 0 0 29 367 0 131 13 201 269 10 0 122 443 82 224 0 208 23 46 242 11524 250 66 264 20 691 176 70 159 427 485 171 39 37 115 56 122 95 508 171 69 141 147 19 18 72 412 129 50 441 129 222 15 125 72 71 10 89 41 145 26 151 154 8831 1015 0 6 0 24 1204 29 34 0 318 1337 396 327 0 0 28 158 38 1 508 19 14 42 9 317 0 14 57 2 19 17 112 209 59 229 261 15 112 81 177 491 122 23 1 347 238 13 54 83 79 493 138 0 22 0 0 344 62 11037 1 718 1696 13 0 0 39 36 0 52 154 120 2 15 2815 3156 176 15566 15 0 1 111 0 23 886 143 54 114 17 0 246 775 1779 30 906 503 1215 13 452 381 25 75 249 12 36 17 0 71 2 1 0 288 155 0 25 7 39 168 303 2 73 0 70 11 515 947 0 332 0 6 4741 4 0 478207 723 647 60 0 133 0 60 2 334 1457 585 15 177 93 177 0 0 559 0 7 42 179 0 380 0 11 2 73 111 1 364 457 492 57 0 358 537 272 315 0 128 4 183 365 15783 17 734 267 67 92 154 0 361 0 216 47208 1 9 7 82 116 419 362 2 19 197 330 1070 303 0 573 1 24 4 40 8 860 23 2400 247 1106 2014 941 84 64 0 354 4993 46 192 395 264 127 230 169 841 0 0 1487 0 101 3009 23 79 145 0 113 358 10184 1565 216 38373 45 53 43 253 100 70 75 1110 0 3488 206 0 304 1271 297 318 0 37 0 5 4 0 88 81 4 10 28 6 115 3 343 223 622 2078 913 198 302 160 171 31 162 41 141 151 88 4 577 674 312 10 189 714 219 162 151 84 89 130 10 1445 156 186 0 29 38 111 24 83 10 210 3 67 38 0 26 0 309 0 5017 314 151 3 962 237 72 6 48 68 0 11 64 125 154 485 67 328 106 18 0 547 736 12 108 6 0 5 139 314 0 53 10 1208 391 5 119 15 17 41 0 12 126 4323 84 75 3971 141 272 292 164 0 273 5333 0 6 0 105 100 0 1 19 0 2 32 25 8 12458 413 48 0 182 81 12 9 87 59

Calculate relative abundance, and bind back to metadata.

GenusOnly.Counts.Filt.t.wtotal <- GenusOnly.Counts.Filt.t %>%
  mutate(Total.Counts = rowSums(GenusOnly.Counts.Filt.t[,2:ncol(GenusOnly.Counts.Filt.t)]))

dim(GenusOnly.Counts.Filt.t.wtotal)
## [1]  60 897
# create rel abund df
RelAbund.Genus.Filt <- GenusOnly.Counts.Filt.t.wtotal[,2:896]/GenusOnly.Counts.Filt.t.wtotal$Total.Counts

# add back metadata
RelAbund.Genus.Filt <- bind_cols(AllSamples.Metadata, RelAbund.Genus.Filt)

Counting missing data

The goal of these next bits of code are to understand how many missing values we have in our dataset, to set what parameters we will use for filtering.

# how many zeros are in the column AHJD-like viruses?
sum(RelAbund.Genus.Filt$`AHJD-like viruses` == 0)  # this code works
## [1] 31
# remove metadata   
# metadata is all character or factor, so can select only numeric columns
RelAbund.Genus.Filt.nometadata <- RelAbund.Genus.Filt %>%
  select_if(is.numeric)

# create a list with the number of zeros for each genus
counting_zeros <- sapply(RelAbund.Genus.Filt.nometadata, 
                         function(x){ (sum(x==0))})

# plot a histogram to look at missing values
counting_zeros_df <- as.data.frame(counting_zeros)

hist(counting_zeros_df$counting_zeros, 
     breaks = 61,
     main = "Histogram of Genera with Zero Relative Intensity",
     sub = "Starting at No Zeros",
     xlab = "Number of zero relative intensity values",
     ylab = "Frequency")

First column is no missing values, and its so big its hard to see how many missing values we actually have.

# plot a histogram to look, but removing genera that are only missing 1 value
counting_zeros_df_missingval <- counting_zeros_df %>%
  rownames_to_column(var = "rowname") %>%
  filter(counting_zeros > 0) %>%
  column_to_rownames(var = "rowname")

# how many genera have at least one missing value?
dim(counting_zeros_df_missingval)
## [1] 186   1

186 genera have at least one missing value.

# histogram of number of zeros, starting at 1 zero
hist(counting_zeros_df_missingval$counting_zeros, 
     breaks = 60,
     main = "Histogram of Genera with Zero Relative Intensity",
     sub = "Starting at 1 Zero",
     xlab = "Number of zero relative intensity values",
     ylab = "Frequency")

# plot a histogram to look, but removing genera that have 20 or more zeros
counting_zeros_df_missing20ormore <- counting_zeros_df %>%
  rownames_to_column(var = "rowname") %>%
  filter(counting_zeros >= 20) %>%
  column_to_rownames(var = "rowname")

# histogram of number of zeros, starting at 20 zero
hist(counting_zeros_df_missing20ormore$counting_zeros, 
     breaks = 40,
     main = "Histogram of Genera with Zero Relative Intensity",
     sub = "Starting at 20 Zero",
     xlab = "Number of zero relative intensity values",
     ylab = "Frequency")

# how many genera have 20 or more missing value?
dim(counting_zeros_df_missing20ormore)
## [1] 140   1

There are 140 genera that have 20 or more missing values. Because 20 missing values here is 1/3 missing, we decided to use this as our cutoff.

Filter for <33% missingness

Our decided criteria:
Filter out genera from relative abundance table that have > 20 zeros, or more than 33% missing data.

# make a character vector of genera names that have > 20 zeros from the rownames in above table
zeros.20 <- c(rownames(counting_zeros_df_missing20ormore))

# filter using this list
RelAbund.Genus.Filt.zerofilt <- RelAbund.Genus.Filt %>%
  rownames_to_column(var = "rowname") %>%
  select(everything(), -all_of(zeros.20)) %>%
  column_to_rownames(var = "rowname")

RelAbund.Genus.Filt.zerofilt[1:3,1:6]
##                                 Sample_Name Pig    Diet Time_Point
## 1 ShotgunWGS-ControlPig6GutMicrobiome-Day14   6 Control     Day 14
## 2  ShotgunWGS-ControlPig8GutMicrobiome-Day0   8 Control      Day 0
## 3 ShotgunWGS-ControlPig3GutMicrobiome-Day14   3 Control     Day 14
##   Diet_By_Time_Point Abiotrophia
## 1     Control Day 14 0.001305713
## 2      Control Day 0 0.001347804
## 3     Control Day 14 0.001066255
dim(RelAbund.Genus.Filt.zerofilt)
## [1]  60 760

Our final dataset has 755 genera (because there are 5 columns of metadata).

Write final dataset genus rel abund to .csv this way we have it.

write_csv(RelAbund.Genus.Filt.zerofilt,
          file = "Genus_RelAbund_Final_Filtered_WithMetadata.csv")

Microbiome profile

Wrangling to enable collection of some summary statistics about our microbiome profile, including how many genera belong to different domains, etc.

Wrangling

Grab names of final genera.

# contains inplausible genera removed, but not removed for zeroes
dim(Genus.Counts.Filt)
## [1] 895  66
Genus.Counts.Filt[1:5, 1:10]
## # A tibble: 5 × 10
##   domain    phylum    class order family genus `ShotgunWGS-Co…` `ShotgunWGS-Co…`
##   <chr>     <chr>     <chr> <chr> <chr>  <chr>            <dbl>            <dbl>
## 1 Viruses   unclassi… uncl… Caud… Podov… AHJD…               29                0
## 2 Bacteria  Firmicut… Baci… Lact… Aeroc… Abio…             5067             5661
## 3 Eukaryota unclassi… uncl… uncl… uncla… Acan…                0                0
## 4 Bacteria  Cyanobac… uncl… uncl… uncla… Acar…              271              416
## 5 Bacteria  Firmicut… Clos… Clos… Rumin… Acet…             1988             2981
## # … with 2 more variables: `ShotgunWGS-ControlPig3GutMicrobiome-Day14` <dbl>,
## #   `ShotgunWGS-TomatoPig14GutMicrobiome-Day7` <dbl>
# final filtered data
RelAbund.Genus.Filt.zerofilt[1:5, 1:10]
##                                 Sample_Name Pig    Diet Time_Point
## 1 ShotgunWGS-ControlPig6GutMicrobiome-Day14   6 Control     Day 14
## 2  ShotgunWGS-ControlPig8GutMicrobiome-Day0   8 Control      Day 0
## 3 ShotgunWGS-ControlPig3GutMicrobiome-Day14   3 Control     Day 14
## 4  ShotgunWGS-TomatoPig14GutMicrobiome-Day7  14  Tomato      Day 7
## 5  ShotgunWGS-ControlPig5GutMicrobiome-Day7   5 Control      Day 7
##   Diet_By_Time_Point Abiotrophia Acaryochloris  Acetivibrio  Acetobacter
## 1     Control Day 14 0.001305713  6.983388e-05 0.0005122869 1.700751e-05
## 2      Control Day 0 0.001347804  9.904370e-05 0.0007097339 2.047538e-05
## 3     Control Day 14 0.001066255  6.914992e-05 0.0005363651 1.553931e-05
## 4       Tomato Day 7 0.001311580  1.090209e-04 0.0008422076 3.412106e-05
## 5      Control Day 7 0.001207244  7.488298e-05 0.0006482261 2.083700e-05
##   Acetohalobium
## 1  0.0002669664
## 2  0.0003268918
## 3  0.0002628733
## 4  0.0003320562
## 5  0.0002852065
# grab colnames which have all the final genera
final_genera <- colnames(RelAbund.Genus.Filt.zerofilt)

# remove metadata colnames
final_genera <- final_genera[6:760]  

final_genera <- as.data.frame(final_genera)

# create a df with the final genera we want to keep for our analysis
final_genera <- final_genera %>%
  rename(genus = final_genera)

Get back domain and inner_join() with final_genera list

# pull from full dataset the domain and genus columns
Genus.Counts.Filt.Domain.Genera <- Genus.Counts.Filt %>%
  select(domain, genus)

Genus.Counts.Filt.Domain.Genera[1:10,]
## # A tibble: 10 × 2
##    domain    genus            
##    <chr>     <chr>            
##  1 Viruses   AHJD-like viruses
##  2 Bacteria  Abiotrophia      
##  3 Eukaryota Acanthamoeba     
##  4 Bacteria  Acaryochloris    
##  5 Bacteria  Acetivibrio      
##  6 Bacteria  Acetobacter      
##  7 Bacteria  Acetohalobium    
##  8 Bacteria  Acholeplasma     
##  9 Bacteria  Achromobacter    
## 10 Bacteria  Acidaminococcus
# want to join Genus.Counts.Filt.Domain.Genera with final_genera
final_genera_withdomain <- inner_join(final_genera, Genus.Counts.Filt.Domain.Genera,
                                      by = "genus")

Count genera

final_genera_withdomain %>%
  count()
##     n
## 1 755
final_genera_withdomain %>%
  group_by(domain) %>%
  count()
## # A tibble: 5 × 2
## # Groups:   domain [5]
##   domain              n
##   <chr>           <int>
## 1 Archaea            60
## 2 Bacteria          582
## 3 Eukaryota          89
## 4 other sequences     1
## 5 Viruses            23

We have 755 total genera. We have 60 genera from Archaea, 582 from Bacteria, 89 from Eukaryota, and 23 from Viruses.

Most prevalent genera

What are the most prevalent genera in our pigs?

RelAbund.Genus.Filt.zerofilt[1:5, 1:10]
##                                 Sample_Name Pig    Diet Time_Point
## 1 ShotgunWGS-ControlPig6GutMicrobiome-Day14   6 Control     Day 14
## 2  ShotgunWGS-ControlPig8GutMicrobiome-Day0   8 Control      Day 0
## 3 ShotgunWGS-ControlPig3GutMicrobiome-Day14   3 Control     Day 14
## 4  ShotgunWGS-TomatoPig14GutMicrobiome-Day7  14  Tomato      Day 7
## 5  ShotgunWGS-ControlPig5GutMicrobiome-Day7   5 Control      Day 7
##   Diet_By_Time_Point Abiotrophia Acaryochloris  Acetivibrio  Acetobacter
## 1     Control Day 14 0.001305713  6.983388e-05 0.0005122869 1.700751e-05
## 2      Control Day 0 0.001347804  9.904370e-05 0.0007097339 2.047538e-05
## 3     Control Day 14 0.001066255  6.914992e-05 0.0005363651 1.553931e-05
## 4       Tomato Day 7 0.001311580  1.090209e-04 0.0008422076 3.412106e-05
## 5      Control Day 7 0.001207244  7.488298e-05 0.0006482261 2.083700e-05
##   Acetohalobium
## 1  0.0002669664
## 2  0.0003268918
## 3  0.0002628733
## 4  0.0003320562
## 5  0.0002852065
genera_means <- RelAbund.Genus.Filt.zerofilt %>%
  summarize_if(is.numeric, mean)

genera_means_t <- t(genera_means)
genera_means_t <- as.data.frame(genera_means_t)

genera_means_t <- genera_means_t %>%
  rename(rel_abund_genera = V1) %>%
  arrange(-rel_abund_genera)

head(genera_means_t)
##                  rel_abund_genera
## Prevotella             0.22231328
## Bacteroides            0.10347888
## Clostridium            0.08556113
## Lactobacillus          0.06777787
## Eubacterium            0.05164571
## Faecalibacterium       0.04480044

The most prevalent genera are Prevotella (22.23% average abundance), Bacteroides (10.34%), Clostridium (8.56%), Lactobacillus (6.78%) and Eubacterium (5.16%).

What is the standard deviation of genera with the highest relative abundance?

RelAbund.Genus.Filt.zerofilt[1:5, 1:10]
##                                 Sample_Name Pig    Diet Time_Point
## 1 ShotgunWGS-ControlPig6GutMicrobiome-Day14   6 Control     Day 14
## 2  ShotgunWGS-ControlPig8GutMicrobiome-Day0   8 Control      Day 0
## 3 ShotgunWGS-ControlPig3GutMicrobiome-Day14   3 Control     Day 14
## 4  ShotgunWGS-TomatoPig14GutMicrobiome-Day7  14  Tomato      Day 7
## 5  ShotgunWGS-ControlPig5GutMicrobiome-Day7   5 Control      Day 7
##   Diet_By_Time_Point Abiotrophia Acaryochloris  Acetivibrio  Acetobacter
## 1     Control Day 14 0.001305713  6.983388e-05 0.0005122869 1.700751e-05
## 2      Control Day 0 0.001347804  9.904370e-05 0.0007097339 2.047538e-05
## 3     Control Day 14 0.001066255  6.914992e-05 0.0005363651 1.553931e-05
## 4       Tomato Day 7 0.001311580  1.090209e-04 0.0008422076 3.412106e-05
## 5      Control Day 7 0.001207244  7.488298e-05 0.0006482261 2.083700e-05
##   Acetohalobium
## 1  0.0002669664
## 2  0.0003268918
## 3  0.0002628733
## 4  0.0003320562
## 5  0.0002852065
genera_sd <- RelAbund.Genus.Filt.zerofilt %>%
  summarize_if(is.numeric, sd)

genera_sd_t <- t(genera_sd)
genera_sd_t <- as.data.frame(genera_sd_t)

genera_sd_t <- genera_sd_t %>%
  rename(sd_genera = V1) %>%
  arrange(-sd_genera)

head(genera_sd_t)
##                   sd_genera
## Prevotella       0.05410113
## Lactobacillus    0.04690415
## Streptococcus    0.04033402
## Bacteroides      0.01912817
## Faecalibacterium 0.01902410
## Clostridium      0.01803255

The standard deviations of most prevalent genera are Prevotella (5.4%), Bacteroides (1.9%), Clostridium (1.8%), Lactobacillus (4.6%) and Eubacterium (1.0%).

Rarefaction curves

Create tax and OTU tables

This section uses a different package than the rest of the analysis; data and metadata need to be uploaded again and made into format friendly for package.

# tax table
TAX_tab <- Genus.AllSamples.Counts %>% 
  select(Domain = domain, Phylum = phylum,
         Class = class, Order = order,
         Family = family, Genus = genus)

tax_names <- colnames(TAX_tab)

head(TAX_tab)
## # A tibble: 6 × 6
##   Domain    Phylum                                Class       Order Family Genus
##   <chr>     <chr>                                 <chr>       <chr> <chr>  <chr>
## 1 Viruses   unclassified (derived from Viruses)   unclassifi… Caud… Podov… AHJD…
## 2 Bacteria  Firmicutes                            Bacilli     Lact… Aeroc… Abio…
## 3 Eukaryota unclassified (derived from Eukaryota) unclassifi… uncl… uncla… Acan…
## 4 Bacteria  Cyanobacteria                         unclassifi… uncl… uncla… Acar…
## 5 Bacteria  Firmicutes                            Clostridia  Clos… Rumin… Acet…
## 6 Bacteria  Proteobacteria                        Alphaprote… Rhod… Aceto… Acet…
head(tax_names)
## [1] "Domain" "Phylum" "Class"  "Order"  "Family" "Genus"
#  OTU table
OTU_tab <- Genus.AllSamples.Counts[, seq(7, 66)]

head(OTU_tab)
## # A tibble: 6 × 60
##   `ShotgunWGS-ControlPig6Gu…` `ShotgunWGS-Co…` `ShotgunWGS-Co…` `ShotgunWGS-To…`
##                         <dbl>            <dbl>            <dbl>            <dbl>
## 1                          29                0              153                0
## 2                        5067             5661             4117             1576
## 3                           0                0                0                1
## 4                         271              416              267              131
## 5                        1988             2981             2071             1012
## 6                          66               86               60               41
## # … with 56 more variables: `ShotgunWGS-ControlPig5GutMicrobiome-Day7` <dbl>,
## #   `ShotgunWGS-TomatoPig18GutMicrobiome-Day7` <dbl>,
## #   `ShotgunWGS-TomatoPig16GutMicrobiome-Day7` <dbl>,
## #   `ShotgunWGS-ControlPig10GutMicrobiome-Day7` <dbl>,
## #   `ShotgunWGS-ControlPig2GutMicrobiome-Day0` <dbl>,
## #   `ShotgunWGS-TomatoPig18GutMicrobiome-Day0` <dbl>,
## #   `ShotgunWGS-ControlPig10GutMicrobiome-Day0` <dbl>, …

Create metadata

Since metadata is contained in column names, we will parse them from here.

raw_names <- colnames(OTU_tab)
names_table <- data.frame(Raw_names = raw_names)

First, the string will split by the middle hyphen.

names_table <- names_table %>% 
  separate(Raw_names, into = c("Shotgun", "Type", "Day")) %>% 
  select(-Shotgun)

Now, since the character GutMicrobiome is constant over all samples, it will be removed. In the same manner, the character Day will be removed.

names_table <- names_table %>% 
  mutate(Type = str_remove(string = Type, pattern = "GutMicrobiome") ) %>% 
  mutate(Type = str_remove(string = Type, pattern = "GutMicrobime") ) %>% 
  mutate(Day = str_remove(string = Day, pattern = "Day"))
head(names_table, 2)
##          Type Day
## 1 ControlPig6  14
## 2 ControlPig8   0

Since Pig is in the middle of the sample type and the pig number, it will be used as separator character. And the final result is a tidy data.

names_table <- names_table %>% 
  separate(col = Type, into = c("Type", "Pig"), sep = "Pig") %>% 
  mutate(Type = factor(Type), Pig = factor(Pig), Day = as.integer(Day)) %>% 
  select(Type, Day, Pig)
head(names_table)
##      Type Day Pig
## 1 Control  14   6
## 2 Control   0   8
## 3 Control  14   3
## 4  Tomato   7  14
## 5 Control   7   5
## 6  Tomato   7  18

Renaming samples

Now that we have tidy data, it’s better to replace long names with shorter ones. New names will be created as Type_Pig_Day. We are also creating names that distinguish the 6 diet by time point groups.

tmp_names  <- names_table %>%
  mutate(Pig = paste0("P", Pig), Day = paste0("D", Day)) %>% 
  unite("Kronas", Type:Day) %>% unite("Sample", Kronas:Pig, remove = F) %>% 
  select(-Pig)

head(tmp_names)
##           Sample      Kronas
## 1 Control_D14_P6 Control_D14
## 2  Control_D0_P8  Control_D0
## 3 Control_D14_P3 Control_D14
## 4  Tomato_D7_P14   Tomato_D7
## 5  Control_D7_P5  Control_D7
## 6  Tomato_D7_P18   Tomato_D7
metadata <- bind_cols(names_table, tmp_names)
head(metadata)
##      Type Day Pig         Sample      Kronas
## 1 Control  14   6 Control_D14_P6 Control_D14
## 2 Control   0   8  Control_D0_P8  Control_D0
## 3 Control  14   3 Control_D14_P3 Control_D14
## 4  Tomato   7  14  Tomato_D7_P14   Tomato_D7
## 5 Control   7   5  Control_D7_P5  Control_D7
## 6  Tomato   7  18  Tomato_D7_P18   Tomato_D7

Finally, in order to use this metadata with the OTU table, which is linked by names, the row names of the metadata and the column names in the OTU table must be the same.

rownames(names_table) <-  tmp_names$Sample
colnames(OTU_tab) <-   tmp_names$Sample

Creating a phyloseq object

It’s time create a phyloseq object that will allow us to analyze this data easier.

rownames(metadata) <- metadata$Sample
gut_microbiome_raw <- phyloseq(otu_table(OTU_tab, taxa_are_rows = T),
                               tax_table(TAX_tab),
                               sample_data(metadata))

colnames(tax_table(gut_microbiome_raw) ) <- tax_names

We can see that data at genus level accounts with 1085 taxas in 60 samples. But, we had developed a filtering scheme to remove very low abundance and inconsistently detected taxa, so let’s merge this full list

gut_microbiome_raw
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 1085 taxa and 60 samples ]
## sample_data() Sample Data:       [ 60 samples by 5 sample variables ]
## tax_table()   Taxonomy Table:    [ 1085 taxa by 6 taxonomic ranks ]

Filtering taxa

We want to only include the taxa we ended up using in our final analysis. We have already created an df final_genera which contains only the genera used in our final analysis.

final_genera_forphyloseq <- final_genera$genus

# subset to include only final genera
gut_microbiome_clean <- subset_taxa(gut_microbiome_raw, Genus %in% final_genera_forphyloseq)

gut_microbiome_clean
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 755 taxa and 60 samples ]
## sample_data() Sample Data:       [ 60 samples by 5 sample variables ]
## tax_table()   Taxonomy Table:    [ 755 taxa by 6 taxonomic ranks ]

The final phyloseq object has 871 taxonomic levels, in our cases, species since it is the lowest taxonomic levels that the sequences were annotated.

In order to check if we have the 45 phyla, we are gonna to count the unique phyla names in the dataset.

length(unique(tax_table(gut_microbiome_clean)[, "Genus"]))
## [1] 755

We got our 755 genera.

Creating rarefaction curves

plot_rarefaction <- ranacapa::ggrare(gut_microbiome_clean, step = 60000,
                                   color = 'Type',  se = F, plot = F) 
## rarefying sample Control_D14_P6
## rarefying sample Control_D0_P8
## rarefying sample Control_D14_P3
## rarefying sample Tomato_D7_P14
## rarefying sample Control_D7_P5
## rarefying sample Tomato_D7_P18
## rarefying sample Tomato_D7_P16
## rarefying sample Control_D7_P10
## rarefying sample Control_D0_P2
## rarefying sample Tomato_D0_P18
## rarefying sample Control_D0_P10
## rarefying sample Control_D0_P7
## rarefying sample Control_D14_P8
## rarefying sample Tomato_D0_P11
## rarefying sample Tomato_D0_P19
## rarefying sample Tomato_D14_P17
## rarefying sample Control_D14_P9
## rarefying sample Control_D14_P10
## rarefying sample Tomato_D7_P19
## rarefying sample Control_D14_P5
## rarefying sample Control_D7_P2
## rarefying sample Control_D7_P6
## rarefying sample Tomato_D0_P12
## rarefying sample Tomato_D0_P14
## rarefying sample Control_D14_P7
## rarefying sample Tomato_D14_P11
## rarefying sample Tomato_D0_P20
## rarefying sample Control_D0_P9
## rarefying sample Tomato_D7_P11
## rarefying sample Tomato_D7_P13
## rarefying sample Tomato_D0_P17
## rarefying sample Tomato_D14_P19
## rarefying sample Tomato_D0_P13
## rarefying sample Control_D14_P2
## rarefying sample Control_D7_P1
## rarefying sample Tomato_D7_P15
## rarefying sample Tomato_D0_P15
## rarefying sample Tomato_D7_P12
## rarefying sample Tomato_D14_P14
## rarefying sample Tomato_D14_P20
## rarefying sample Control_D0_P1
## rarefying sample Control_D14_P4
## rarefying sample Control_D0_P6
## rarefying sample Tomato_D0_P16
## rarefying sample Tomato_D14_P16
## rarefying sample Tomato_D14_P18
## rarefying sample Control_D7_P7
## rarefying sample Control_D7_P4
## rarefying sample Tomato_D14_P13
## rarefying sample Control_D7_P8
## rarefying sample Tomato_D14_P15
## rarefying sample Tomato_D14_P12
## rarefying sample Tomato_D7_P20
## rarefying sample Control_D14_P1
## rarefying sample Control_D0_P3
## rarefying sample Control_D0_P5
## rarefying sample Control_D0_P4
## rarefying sample Control_D7_P9
## rarefying sample Control_D7_P3
## rarefying sample Tomato_D7_P17
plot_rarefaction <-  plot_rarefaction + theme_test() +
  facet_wrap("Day", scales = "free_x", ncol = 1, 
             labeller = labeller(Day = c( `0` = "Day 0" ,
                                          `7` = "Day 7",
                                          `14`= "Day 14")) ) + 
  labs(color = "Diet",
       title = "Rarefaction curves") +
  scale_color_manual(values = c( "steelblue2", "tomato2"))
  
plot_rarefaction

Krona plots for exploratory analysis

The psadd package is able to create Krona plots with an phyloseq object. Two Kronas will be created, per sample and per category Day + Type. The Krona plots only include the final filtered taxa we used in our analysis.

# Write kronas per samples
plot_krona(physeq = gut_microbiome_clean, 
           output = "kronas/per_sample", 
           variable = "Sample")

# Write kronas per category (Sample type + Day) i.e. Tomato_D7
plot_krona(physeq = gut_microbiome_clean, 
           output = "kronas/per_category", 
           variable = "Kronas")

PERMANOVA

Use PERMANOVA to conduct statistical analysis of overall microbial profile differences among groups.

All samples, full model

Test the overall effect of Diet, Time_Point and their interaction of the overall microbiome. ORIGINAL

# create factors
factors_time_diet_pig <- RelAbund.Genus.Filt.zerofilt %>% select(Time_Point, Diet, Pig)

# create permutations
perm_time_diet_pig <- how(nperm = 9999)
setBlocks(perm_time_diet_pig) <- with(factors_time_diet_pig, Pig)

# run permanova
AllData.Genus.Filt.permanova <- adonis2(RelAbund.Genus.Filt.zerofilt[,-c(1:5)]~Diet*Time_Point,
                                        data = factors_time_diet_pig,
                                        permutations = perm_time_diet_pig,
                                        method = "bray")

AllData.Genus.Filt.permanova
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Blocks:  with(factors_time_diet_pig, Pig) 
## Permutation: free
## Number of permutations: 9999
## 
## adonis2(formula = RelAbund.Genus.Filt.zerofilt[, -c(1:5)] ~ Diet * Time_Point, data = factors_time_diet_pig, permutations = perm_time_diet_pig, method = "bray")
##                 Df SumOfSqs      R2      F Pr(>F)    
## Diet             1  0.05879 0.04954 3.4114 0.0001 ***
## Time_Point       2  0.16612 0.13999 4.8196 0.0001 ***
## Diet:Time_Point  2  0.03113 0.02623 0.9031 0.3831    
## Residual        54  0.93061 0.78424                  
## Total           59  1.18665 1.00000                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
  • Diet: p = 0.0001, significant
  • Time_Point: p = 0.0001, significant
  • Diet*Time_Point: p = 0.3831, non-significant

Comparison when you don’t filter out for missing values

AllData.Genus.Filt.permanova.no0filt <- adonis2(RelAbund.Genus.Filt[,-c(1:5)]~Diet*Time_Point,
                                        data = factors_time_diet_pig,
                                        permutations = perm_time_diet_pig,
                                        method = "bray")
AllData.Genus.Filt.permanova.no0filt
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Blocks:  with(factors_time_diet_pig, Pig) 
## Permutation: free
## Number of permutations: 9999
## 
## adonis2(formula = RelAbund.Genus.Filt[, -c(1:5)] ~ Diet * Time_Point, data = factors_time_diet_pig, permutations = perm_time_diet_pig, method = "bray")
##                 Df SumOfSqs      R2      F Pr(>F)    
## Diet             1  0.05880 0.04954 3.4114 0.0001 ***
## Time_Point       2  0.16616 0.14000 4.8200 0.0001 ***
## Diet:Time_Point  2  0.03113 0.02623 0.9031 0.3750    
## Residual        54  0.93079 0.78423                  
## Total           59  1.18689 1.00000                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
  • Diet: p = 0.0001, significant
  • Time_Point: p = 0.0001, significant
  • Diet*Time_Point: p = 0.3750, non-significant

Significance is the same whether you filter for missing data or not.

NEW

set.seed(2021)
# create factors
factors_time_diet_pig_genus <- RelAbund.Genus.Filt.zerofilt %>% select(Time_Point, Diet, Pig)

# create permutations
perm_time_diet_pig_genus <- how(within = Within(type="series", constant=TRUE),
                                plots = Plots(strata=factors_time_diet_pig_genus$Pig,
                                              type="free",))
# run permanova
AllData.Genus.Filt.permanova <- adonis2(RelAbund.Genus.Filt.zerofilt[,-c(1:5)]~Diet*Time_Point,
                                        data = factors_time_diet_pig_genus,
                                        permutations = perm_time_diet_pig_genus,
                                        method = "bray",
                                        by = "margin")

AllData.Genus.Filt.permanova
## Permutation test for adonis under reduced model
## Marginal effects of terms
## Plots: factors_time_diet_pig_genus$Pig, plot permutation: free
## Permutation: series constant permutation within each Plot
## Number of permutations: 199
## 
## adonis2(formula = RelAbund.Genus.Filt.zerofilt[, -c(1:5)] ~ Diet * Time_Point, data = factors_time_diet_pig_genus, permutations = perm_time_diet_pig_genus, method = "bray", by = "margin")
##                 Df SumOfSqs      R2      F Pr(>F)
## Diet:Time_Point  2  0.03113 0.02623 0.9031   0.36
## Residual        54  0.93061 0.78424              
## Total           59  1.18665 1.00000

Interaction not significant (p=.355) so remove from model

set.seed(2021)
# create factors
factors_time_diet_pig_genus <- RelAbund.Genus.Filt.zerofilt %>% select(Time_Point, Diet, Pig)

# create permutations
perm_time_diet_pig_genus <- how(within = Within(type="series", constant=TRUE),
                                plots = Plots(strata=factors_time_diet_pig_genus$Pig, type="free",))
# run permanova
AllData.Genus.Filt.permanova <- adonis2(RelAbund.Genus.Filt.zerofilt[,-c(1:5)]~Diet+Time_Point,
                                        data = factors_time_diet_pig_genus,
                                        permutations = perm_time_diet_pig_genus,
                                        method = "bray",
                                        by = "margin")

AllData.Genus.Filt.permanova
## Permutation test for adonis under reduced model
## Marginal effects of terms
## Plots: factors_time_diet_pig_genus$Pig, plot permutation: free
## Permutation: series constant permutation within each Plot
## Number of permutations: 199
## 
## adonis2(formula = RelAbund.Genus.Filt.zerofilt[, -c(1:5)] ~ Diet + Time_Point, data = factors_time_diet_pig_genus, permutations = perm_time_diet_pig_genus, method = "bray", by = "margin")
##            Df SumOfSqs      R2      F Pr(>F)   
## Diet        1  0.05879 0.04954 3.4232  0.060 . 
## Time_Point  2  0.16612 0.13999 4.8364  0.005 **
## Residual   56  0.96174 0.81047                 
## Total      59  1.18665 1.00000                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Diet not significant p=.060 but close Time significant p=.005

Test for homogeneity of multivariate dispersions

dis <- vegdist(RelAbund.Genus.Filt.zerofilt[,-c(1:5)], method = "bray")
mod <- betadisper(dis, RelAbund.Genus.Filt.zerofilt$Diet)
permutest(mod)
## 
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 999
## 
## Response: Distances
##           Df   Sum Sq   Mean Sq      F N.Perm Pr(>F)
## Groups     1 0.001513 0.0015128 1.0653    999  0.332
## Residuals 58 0.082364 0.0014201
dis <- vegdist(RelAbund.Genus.Filt.zerofilt[,-c(1:5)], method = "bray")
mod <- betadisper(dis, RelAbund.Genus.Filt.zerofilt$Time)
permutest(mod)
## 
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 999
## 
## Response: Distances
##           Df   Sum Sq   Mean Sq      F N.Perm Pr(>F)  
## Groups     2 0.008867 0.0044335 2.6538    999  0.081 .
## Residuals 57 0.095227 0.0016707                       
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

MANOVA TRIAL

a <- do.call(rbind, lapply(RelAbund.Genus.Filt.zerofilt, as.data.frame))

dep_vars <- cbind(RelAbund.Genus.Filt.zerofilt[-c(1:5)])
fit <- manova(cbind(RelAbund.Genus.Filt.zerofilt$Abiotrophia,RelAbund.Genus.Filt.zerofilt$Acaryochloris)~Diet*Time_Point + (1|Pig), data=RelAbund.Genus.Filt.zerofilt)

tidy(fit)

Post Hoc PERMANOVA within Each Diet

Within Control Diet Only

Effect in control diet of time.

set.seed(2021)
# filter for only control
control.RelAbund.Genus.Filt.zerofilt <- subset(RelAbund.Genus.Filt.zerofilt, Diet == "Control")

# create factors
factors_control_genera <- droplevels(control.RelAbund.Genus.Filt.zerofilt %>% select(Time_Point, Pig))

# create permutations
perm_control_genera <- how(within = Within(type="series", constant=TRUE),
                                plots = Plots(strata=factors_control_genera$Pig, type="none",))
# run permanova
Control.ByTime.Genus.zerofilt <- adonis2(control.RelAbund.Genus.Filt.zerofilt[,-c(1:5)]~Time_Point,
                                        data = factors_control_genera,
                                        permutations = perm_control_genera,
                                        method = "bray",
                                        by = "margin")
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
Control.ByTime.Genus.zerofilt
## Permutation test for adonis under NA model
## Marginal effects of terms
## Plots: factors_control_genera$Pig, plot permutation: none
## Permutation: series constant permutation within each Plot
## Number of permutations: 2
## 
## adonis2(formula = control.RelAbund.Genus.Filt.zerofilt[, -c(1:5)] ~ Time_Point, data = factors_control_genera, permutations = perm_control_genera, method = "bray", by = "margin")
##            Df SumOfSqs      R2     F Pr(>F)
## Time_Point  2  0.13507 0.22578 3.937 0.3333
## Residual   27  0.46317 0.77422             
## Total      29  0.59824 1.00000

Significant effect of time (p = 0.005) within control samples.

Now do pairwise comparisons to see where the significance is coming from

Control T1 vs Control T2
set.seed(2021)
# filter data set for only samples at T1 and T2
control.T1T2.RelAbund.Genus.Filt.zerofilt <- subset(control.RelAbund.Genus.Filt.zerofilt,
                                               Time_Point != "Day 14")

# create factors
factors_control_T1T2_pig_genus <- droplevels(control.T1T2.RelAbund.Genus.Filt.zerofilt %>%
                                               select(Time_Point, Pig))

# create permutations
perm_control_T1T2_pig_genus <- how(within = Within(type="series", constant=TRUE),
                                   plots = Plots(strata=factors_control_T1T2_pig_genus$Pig,
                                                 type = "free"))

# run PERMANOVA
Control.T1T2.Genus.zerofilt.permanova <- adonis2(control.T1T2.RelAbund.Genus.Filt.zerofilt[,-c(1:5)]~Time_Point,
                                                 data = factors_control_T1T2_pig_genus,
                                                 permutations = perm_control_T1T2_pig_genus, 
                                                 method = "bray",
                                                 by = "margin")

Control.T1T2.Genus.zerofilt.permanova
## Permutation test for adonis under NA model
## Marginal effects of terms
## Plots: factors_control_T1T2_pig_genus$Pig, plot permutation: free
## Permutation: series constant permutation within each Plot
## Number of permutations: 199
## 
## adonis2(formula = control.T1T2.RelAbund.Genus.Filt.zerofilt[, -c(1:5)] ~ Time_Point, data = factors_control_T1T2_pig_genus, permutations = perm_control_T1T2_pig_genus, method = "bray", by = "margin")
##            Df SumOfSqs      R2      F Pr(>F)  
## Time_Point  1  0.03492 0.08631 1.7003   0.03 *
## Residual   18  0.36971 0.91369                
## Total      19  0.40464 1.00000                
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Significant p = .030

Control T1 vs T3
set.seed(2021)
# filter data set for only samples at T1 and T3
control.T1T3.RelAbund.Genus.Filt.zerofilt <- subset(control.RelAbund.Genus.Filt.zerofilt,
                                               Time_Point != "Day 7")

# create factors
factors_control_T1T3_pig_genus <- droplevels(control.T1T3.RelAbund.Genus.Filt.zerofilt %>%
                                               select(Time_Point, Pig))

# create permutations
perm_control_T1T3_pig_genus <- how(within = Within(type="series", constant=TRUE),
                                   plots = Plots(strata=factors_control_T1T3_pig_genus$Pig,
                                                 type = "free"))

# run PERMANOVA
Control.T1T3.Genus.zerofilt.permanova <- adonis2(control.T1T3.RelAbund.Genus.Filt.zerofilt[,-c(1:5)]~Time_Point,
                                                 data = factors_control_T1T3_pig_genus,
                                                 permutations = perm_control_T1T3_pig_genus, 
                                                 method = "bray",
                                                 by = "margin")

Control.T1T3.Genus.zerofilt.permanova
## Permutation test for adonis under NA model
## Marginal effects of terms
## Plots: factors_control_T1T3_pig_genus$Pig, plot permutation: free
## Permutation: series constant permutation within each Plot
## Number of permutations: 199
## 
## adonis2(formula = control.T1T3.RelAbund.Genus.Filt.zerofilt[, -c(1:5)] ~ Time_Point, data = factors_control_T1T3_pig_genus, permutations = perm_control_T1T3_pig_genus, method = "bray", by = "margin")
##            Df SumOfSqs      R2      F Pr(>F)   
## Time_Point  1  0.09340 0.28463 7.1617   0.01 **
## Residual   18  0.23474 0.71537                 
## Total      19  0.32814 1.00000                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Significant p = .010

Control T2 vs T3
set.seed(2021)
# filter data set for only samples at T1 and T3
control.T2T3.RelAbund.Genus.Filt.zerofilt <- subset(control.RelAbund.Genus.Filt.zerofilt,
                                               Time_Point != "Day 0")

# create factors
factors_control_T2T3_pig_genus <- droplevels(control.T2T3.RelAbund.Genus.Filt.zerofilt %>%
                                               select(Time_Point, Pig))

# create permutations
perm_control_T2T3_pig_genus <- how(within = Within(type="series", constant=TRUE),
                                   plots = Plots(strata=factors_control_T2T3_pig_genus$Pig,
                                                 type = "free"))

# run PERMANOVA
Control.T2T3.Genus.zerofilt.permanova <- adonis2(control.T2T3.RelAbund.Genus.Filt.zerofilt[,-c(1:5)]~Time_Point,
                                                 data = factors_control_T2T3_pig_genus,
                                                 permutations = perm_control_T2T3_pig_genus, 
                                                 method = "bray",
                                                 by = "margin")

Control.T2T3.Genus.zerofilt.permanova
## Permutation test for adonis under NA model
## Marginal effects of terms
## Plots: factors_control_T2T3_pig_genus$Pig, plot permutation: free
## Permutation: series constant permutation within each Plot
## Number of permutations: 199
## 
## adonis2(formula = control.T2T3.RelAbund.Genus.Filt.zerofilt[, -c(1:5)] ~ Time_Point, data = factors_control_T2T3_pig_genus, permutations = perm_control_T2T3_pig_genus, method = "bray", by = "margin")
##            Df SumOfSqs      R2      F Pr(>F)  
## Time_Point  1  0.07429 0.18752 4.1544  0.015 *
## Residual   18  0.32189 0.81248                
## Total      19  0.39618 1.00000                
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Sig: p=.015

Tomato

Effect of tomato diet over time.

set.seed(2021)
# filter for only tomato
tomato.RelAbund.Genus.Filt.zerofilt <- subset(RelAbund.Genus.Filt.zerofilt, Diet == "Tomato")

# create factors
factors_tomato_genera <- droplevels(tomato.RelAbund.Genus.Filt.zerofilt %>% select(Time_Point, Pig))

# create permutations
perm_tomato_genera <- how(within = Within(type="series", constant=TRUE),
                          plots = Plots(strata=factors_tomato_genera$Pig, type="none",))
# run permanova
Tomato.ByTime.Genus.zerofilt <- adonis2(tomato.RelAbund.Genus.Filt.zerofilt[,-c(1:5)]~Time_Point,
                                        data = factors_tomato_genera,
                                        permutations = perm_tomato_genera,
                                        method = "bray",
                                        by = "margin")
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
Tomato.ByTime.Genus.zerofilt
## Permutation test for adonis under NA model
## Marginal effects of terms
## Plots: factors_tomato_genera$Pig, plot permutation: none
## Permutation: series constant permutation within each Plot
## Number of permutations: 2
## 
## adonis2(formula = tomato.RelAbund.Genus.Filt.zerofilt[, -c(1:5)] ~ Time_Point, data = factors_tomato_genera, permutations = perm_tomato_genera, method = "bray", by = "margin")
##            Df SumOfSqs      R2      F Pr(>F)
## Time_Point  2  0.06217 0.11739 1.7955 0.3333
## Residual   27  0.46745 0.88261              
## Total      29  0.52962 1.00000

Significant effect of time (p = 0.01) within tomato samples

Now do pairwise comparisons to see where the significance is coming from

Tomato T1 vs Tomato T2
set.seed(2021)
# filter data set for only samples at T1 and T2
tomato.T1T2.RelAbund.Genus.Filt.zerofilt <- subset(tomato.RelAbund.Genus.Filt.zerofilt,
                                               Time_Point != "Day 14")

# create factors
factors_tomato_T1T2_pig_genus <- droplevels(tomato.T1T2.RelAbund.Genus.Filt.zerofilt %>%
                                               select(Time_Point, Pig))

# create permutations
perm_tomato_T1T2_pig_genus <- how(within = Within(type="series", constant=TRUE),
                                   plots = Plots(strata=factors_tomato_T1T2_pig_genus$Pig,
                                                 type = "free"))

# run PERMANOVA
tomato.T1T2.Genus.zerofilt.permanova <- adonis2(tomato.T1T2.RelAbund.Genus.Filt.zerofilt[,-c(1:5)]~Time_Point,
                                                 data = factors_tomato_T1T2_pig_genus,
                                                 permutations = perm_tomato_T1T2_pig_genus, 
                                                 method = "bray",
                                                 by = "margin")

tomato.T1T2.Genus.zerofilt.permanova
## Permutation test for adonis under NA model
## Marginal effects of terms
## Plots: factors_tomato_T1T2_pig_genus$Pig, plot permutation: free
## Permutation: series constant permutation within each Plot
## Number of permutations: 199
## 
## adonis2(formula = tomato.T1T2.RelAbund.Genus.Filt.zerofilt[, -c(1:5)] ~ Time_Point, data = factors_tomato_T1T2_pig_genus, permutations = perm_tomato_T1T2_pig_genus, method = "bray", by = "margin")
##            Df SumOfSqs      R2      F Pr(>F)  
## Time_Point  1  0.02556 0.07483 1.4559   0.09 .
## Residual   18  0.31598 0.92517                
## Total      19  0.34154 1.00000                
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Not significant .090

tomato T1 vs T3
set.seed(2021)
# filter data set for only samples at T1 and T3
tomato.T1T3.RelAbund.Genus.Filt.zerofilt <- subset(tomato.RelAbund.Genus.Filt.zerofilt,
                                               Time_Point != "Day 7")

# create factors
factors_tomato_T1T3_pig_genus <- droplevels(tomato.T1T3.RelAbund.Genus.Filt.zerofilt %>%
                                               select(Time_Point, Pig))

# create permutations
perm_tomato_T1T3_pig_genus <- how(within = Within(type="series", constant=TRUE),
                                   plots = Plots(strata=factors_tomato_T1T3_pig_genus$Pig,
                                                 type = "free"))

# run PERMANOVA
tomato.T1T3.Genus.zerofilt.permanova <- adonis2(tomato.T1T3.RelAbund.Genus.Filt.zerofilt[,-c(1:5)]~Time_Point,
                                                 data = factors_tomato_T1T3_pig_genus,
                                                 permutations = perm_tomato_T1T3_pig_genus, 
                                                 method = "bray",
                                                 by = "margin")

tomato.T1T3.Genus.zerofilt.permanova
## Permutation test for adonis under NA model
## Marginal effects of terms
## Plots: factors_tomato_T1T3_pig_genus$Pig, plot permutation: free
## Permutation: series constant permutation within each Plot
## Number of permutations: 199
## 
## adonis2(formula = tomato.T1T3.RelAbund.Genus.Filt.zerofilt[, -c(1:5)] ~ Time_Point, data = factors_tomato_T1T3_pig_genus, permutations = perm_tomato_T1T3_pig_genus, method = "bray", by = "margin")
##            Df SumOfSqs      R2      F Pr(>F)
## Time_Point  1  0.04969 0.14718 3.1064   0.15
## Residual   18  0.28793 0.85282              
## Total      19  0.33762 1.00000

Significant p = .150

tomato T2 vs T3
set.seed(2021)
# filter data set for only samples at T1 and T3
tomato.T2T3.RelAbund.Genus.Filt.zerofilt <- subset(tomato.RelAbund.Genus.Filt.zerofilt,
                                               Time_Point != "Day 0")

# create factors
factors_tomato_T2T3_pig_genus <- droplevels(tomato.T2T3.RelAbund.Genus.Filt.zerofilt %>%
                                               select(Time_Point, Pig))

# create permutations
perm_tomato_T2T3_pig_genus <- how(within = Within(type="series", constant=TRUE),
                                   plots = Plots(strata=factors_tomato_T2T3_pig_genus$Pig,
                                                 type = "free"))

# run PERMANOVA
tomato.T2T3.Genus.zerofilt.permanova <- adonis2(tomato.T2T3.RelAbund.Genus.Filt.zerofilt[,-c(1:5)]~Time_Point,
                                                 data = factors_tomato_T2T3_pig_genus,
                                                 permutations = perm_tomato_T2T3_pig_genus, 
                                                 method = "bray",
                                                 by = "margin")

tomato.T2T3.Genus.zerofilt.permanova
## Permutation test for adonis under NA model
## Marginal effects of terms
## Plots: factors_tomato_T2T3_pig_genus$Pig, plot permutation: free
## Permutation: series constant permutation within each Plot
## Number of permutations: 199
## 
## adonis2(formula = tomato.T2T3.RelAbund.Genus.Filt.zerofilt[, -c(1:5)] ~ Time_Point, data = factors_tomato_T2T3_pig_genus, permutations = perm_tomato_T2T3_pig_genus, method = "bray", by = "margin")
##            Df SumOfSqs     R2      F Pr(>F)
## Time_Point  1  0.01801 0.0516 0.9794   0.12
## Residual   18  0.33098 0.9484              
## Total      19  0.34899 1.0000

p is non significant =.120

Subset by time

Day 0

Effect of diet at day 0.

# filter for day 0 only
d0.RelAbund.Genus.Filt.zerofilt <- subset(RelAbund.Genus.Filt.zerofilt, Time_Point == "Day 0")

# create factors
# don't need to include pig, since no repeated measures here 
# only testing Diet within a time point
factors_day0_genera <- d0.RelAbund.Genus.Filt.zerofilt %>% 
  select(Diet)

# create permutations
perm_day0_genera <- how(nperm = 9999)

# run PERMANOVA
d0.ByTime.Genus.zerofilt <- adonis2(d0.RelAbund.Genus.Filt.zerofilt[,-c(1:5)]~Diet,
                                         data = factors_day0_genera,
                                         permutations = perm_day0_genera,
                                         method = "bray")
d0.ByTime.Genus.zerofilt
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 9999
## 
## adonis2(formula = d0.RelAbund.Genus.Filt.zerofilt[, -c(1:5)] ~ Diet, data = factors_day0_genera, permutations = perm_day0_genera, method = "bray")
##          Df SumOfSqs     R2      F Pr(>F)
## Diet      1 0.020138 0.0676 1.3051 0.2462
## Residual 18 0.277747 0.9324              
## Total    19 0.297885 1.0000

Non-significant effect of Diet (p = 0.2402) at day 0.

Day 7

Effect of diet at day 7.

# filter for day 7 only
d7.RelAbund.Genus.Filt.zerofilt <- subset(RelAbund.Genus.Filt.zerofilt, Time_Point == "Day 7")

# create factors
# don't need to include pig, since no repeated measures here 
# only testing Diet within a time point
factors_day7_genera <- d7.RelAbund.Genus.Filt.zerofilt %>% 
  select(Diet)

# create permutations
perm_day7_genera <- how(nperm = 9999)

# run PERMANOVA
d7.ByTime.Genus.zerofilt <- adonis2(d7.RelAbund.Genus.Filt.zerofilt[,-c(1:5)]~Diet,
                                         data = factors_day7_genera,
                                         permutations = perm_day7_genera,
                                         method = "bray")
d7.ByTime.Genus.zerofilt
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 9999
## 
## adonis2(formula = d7.RelAbund.Genus.Filt.zerofilt[, -c(1:5)] ~ Diet, data = factors_day7_genera, permutations = perm_day7_genera, method = "bray")
##          Df SumOfSqs     R2      F Pr(>F)
## Diet      1  0.02780 0.0638 1.2267 0.2762
## Residual 18  0.40795 0.9362              
## Total    19  0.43575 1.0000

Non-significant effect of Diet (p = 0.2836) at day 7.

Day 14

Effect of diet at day 14.

# filter for day 14 only
d14.RelAbund.Genus.Filt.zerofilt <- subset(RelAbund.Genus.Filt.zerofilt, Time_Point == "Day 14")

# create factors
# don't need to include pig, since no repeated measures here 
# only testing Diet within a time point
factors_day14_genera <- d14.RelAbund.Genus.Filt.zerofilt %>% 
  select(Diet)

# create permutations
perm_day14_genera <- how(nperm = 9999)

# run PERMANOVA
d14.ByTime.Genus.zerofilt <- adonis2(d14.RelAbund.Genus.Filt.zerofilt[,-c(1:5)]~Diet,
                                     data = factors_day14_genera,
                                     permutations = perm_day14_genera,
                                     method = "bray")
d14.ByTime.Genus.zerofilt
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 9999
## 
## adonis2(formula = d14.RelAbund.Genus.Filt.zerofilt[, -c(1:5)] ~ Diet, data = factors_day14_genera, permutations = perm_day14_genera, method = "bray")
##          Df SumOfSqs      R2     F Pr(>F)   
## Diet      1 0.041978 0.14631 3.085 0.0062 **
## Residual 18 0.244923 0.85369                
## Total    19 0.286900 1.00000                
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Significant effect of Diet (p = 0.005) at day 14.

PCoA Beta Diversity

All samples

# calculate distances
genus.filt.dist.20zeros <- vegdist(RelAbund.Genus.Filt.zerofilt[6:ncol(RelAbund.Genus.Filt.zerofilt)], 
                                   method = "bray")

# do multi-dimensional scaling (the PCoA calculations) on those distances
scale.genus.filt.20zeros <- cmdscale(genus.filt.dist.20zeros, k=2)

# make into data frame
scale.genus.filt.df.20zeros <- as.data.frame(cbind(scale.genus.filt.20zeros, 
                                                   AllSamples.Metadata))

# do PCoA again, but get eigen values
scale.genus.filt.20zeros.eig <- cmdscale(genus.filt.dist.20zeros, k=2, eig = TRUE)

# convert eigenvalues to percentages and assign to a variable
eigs.genus.filt.20zeros <- (100* ((scale.genus.filt.20zeros.eig$eig)/(sum(scale.genus.filt.20zeros.eig$eig))))

# round the converted eigenvalues
round.eigs.genus.20zeros <- round(eigs.genus.filt.20zeros, 3)

All samples, one PCoA

PCoA_genera_20zeros_allsamples <- scale.genus.filt.df.20zeros %>%
ggplot(aes(x = `1`, y = `2`, fill = Diet_By_Time_Point)) +
  geom_point(size=3, color = "black", shape = 21, alpha = 0.9) +
  scale_fill_manual(values=c("skyblue1", "dodgerblue", "royalblue4", "sienna1","firebrick3","tomato4")) +
  theme_classic() +
  theme(axis.text = element_text(color = "black"))+
  labs(x=paste("PC1: ", round.eigs.genus.20zeros[1], "%"), 
       y=paste("PC2: ", round.eigs.genus.20zeros[2], "%"), 
       fill="Diet & Time Point",
       title = "Beta Diversity",
       subtitle = "Genus Level") 

PCoA_genera_20zeros_allsamples

ggsave("Figures/BetaDiversity_PCoA_Genera_allsamples.png", 
       plot = PCoA_genera_20zeros_allsamples, 
       dpi = 800, 
       width = 10, 
       height = 8)

Re-level factors

scale.genus.filt.df.20zeros <- scale.genus.filt.df.20zeros %>% 
  mutate(Time_Point = fct_relevel(Time_Point, c("Day 0", "Day 7", "Day 14")))
Facet by time point
PCoA_genera_20zeros_facetbytime <- scale.genus.filt.df.20zeros %>%
ggplot(aes(x = `1`, y = `2`, fill = Diet_By_Time_Point)) +
  geom_hline(yintercept = 0, color = "light grey", linetype = "dashed", size = 0.3) +
  geom_vline(xintercept = 0, color = "light grey", linetype = "dashed", size = 0.3) +
  geom_point(size=3, color = "black", shape = 21, alpha = 0.9) +
  scale_fill_manual(values=c("skyblue1", "dodgerblue", "royalblue4", "sienna1","firebrick3","tomato4")) +
  theme_bw() +
  theme(axis.text = element_text(color = "black"),
        strip.background =element_rect(fill="white"),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  labs(x=paste("PC1: ", round.eigs.genus.20zeros[1], "%"), 
       y=paste("PC2: ", round.eigs.genus.20zeros[2], "%"), 
       fill="Diet & Time Point",
       title = "Beta Diversity",
       subtitle = "Genera Level, Subset by Time Point") +
  facet_wrap(~Time_Point)

PCoA_genera_20zeros_facetbytime

ggsave("Figures/BetaDiversity_PCoA_Genera_FacetByTimePoint.png", 
       plot = PCoA_genera_20zeros_facetbytime, 
       dpi = 800, 
       width = 10, 
       height = 6)
Facet by diet
PCoA_genera_20zeros_facetbydiet <- scale.genus.filt.df.20zeros %>%
ggplot(aes(x = `1`, y = `2`, fill = Diet_By_Time_Point)) +
  geom_hline(yintercept = 0, color = "light grey", linetype = "dashed", size = 0.3) +
  geom_vline(xintercept = 0, color = "light grey", linetype = "dashed", size = 0.3) +
  geom_point(size=3, color = "black", shape = 21, alpha = 0.9) +
  scale_fill_manual(values=c("skyblue1", "dodgerblue", "royalblue4", "sienna1","firebrick3","tomato4")) +
  theme_bw() +
  theme(axis.text = element_text(color = "black"),
        strip.background =element_rect(fill="white"),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  labs(x=paste("PC1: ", round.eigs.genus.20zeros[1], "%"), 
       y=paste("PC2: ", round.eigs.genus.20zeros[2], "%"), 
       fill="Diet & Time Point",
       title = "Beta Diversity",
       subtitle = "Genera Level, Subset by Diet") +
  facet_wrap(~Diet)

PCoA_genera_20zeros_facetbydiet

ggsave("Figures/BetaDiversity_PCoA_Genera_FacetByDiet.png", 
       plot = PCoA_genera_20zeros_facetbydiet, 
       dpi = 800, 
       width = 10, 
       height = 6)

Subset

Ended up not using this as part of the paper. Since the input is different here (i.e., the PCoA only has the subset data as an input) the output looks slightly different and we didn’t feel this was the most accurate depction of the data.

Control only

# calculate distances
control.RelAbund.Genus.Filt.zerofilt.dist <- vegdist(control.RelAbund.Genus.Filt.zerofilt[,-c(1:5)], 
                                                     method = "bray")

# calculate to make PCoA
control.scale.genus.filt.20zeros <- cmdscale(control.RelAbund.Genus.Filt.zerofilt.dist, k=2)

# filter metadata
meta.control <- subset(AllSamples.Metadata, Diet == "Control")

# make into data frame and add metadata
control.scale.genus.filt.20zeros.df <- as.data.frame(cbind(meta.control, control.scale.genus.filt.20zeros))

# get eigenvalues
control.scale.genus.filt.20zeros.eig <- cmdscale(control.RelAbund.Genus.Filt.zerofilt.dist, k=2, eig = TRUE)
control.eigs.genus.filt.20zeros <- (100*((control.scale.genus.filt.20zeros.eig$eig)/(sum(control.scale.genus.filt.20zeros.eig$eig))))
control.round.eigs.genus.20zeros <- round(control.eigs.genus.filt.20zeros, 3)

Reset factor levels

control.scale.genus.filt.20zeros.df$Time_Point <- factor(control.scale.genus.filt.20zeros.df$Time_Point, levels = c("Day 0", "Day 7", "Day 14"))

Plot

control.scale.genus.filt.20zeros.df %>%
ggplot(aes(x = `1`, y = `2`, fill = Time_Point)) +
  geom_point(size=3, shape = 21, color = "black", alpha = 0.9) +
  scale_fill_manual(values=c("skyblue1", "dodgerblue", "royalblue4"))+
  theme_classic() +
  theme(axis.text = element_text(color = "black")) +
  labs(x=(paste(control.round.eigs.genus.20zeros[1], "%")), 
       y=(paste(control.round.eigs.genus.20zeros[2], "%")), 
       fill = "Time Point",
       title = "Beta Diversity",
       subtitle = "Genera Level, Control Samples Only")

Tomato only

# calculate distances
tomato.RelAbund.Genus.Filt.zerofilt.dist <- vegdist(tomato.RelAbund.Genus.Filt.zerofilt[,-c(1:5)], method = "bray")

# calculate to make PCoA
tomato.scale.genus.filt.20zeros <- cmdscale(tomato.RelAbund.Genus.Filt.zerofilt.dist, k=2)

# filter metadata
meta.tomato <- subset(AllSamples.Metadata, Diet == "Tomato")

# make into data frame and add metadata
tomato.scale.genus.filt.20zeros.df <- as.data.frame(cbind(meta.tomato, tomato.scale.genus.filt.20zeros))

# get eigenvalues
tomato.scale.genus.filt.20zeros.eig <- cmdscale(tomato.RelAbund.Genus.Filt.zerofilt.dist, k=2, eig = TRUE)
tomato.eigs.genus.filt.20zeros <- (100*((tomato.scale.genus.filt.20zeros.eig$eig)/(sum(tomato.scale.genus.filt.20zeros.eig$eig))))
tomato.round.eigs.genus.20zeros <- round(tomato.eigs.genus.filt.20zeros, 3)

Reset factor levels

tomato.scale.genus.filt.20zeros.df$Time_Point <- factor(tomato.scale.genus.filt.20zeros.df$Time_Point, levels = c("Day 0", "Day 7", "Day 14"))

Plot

tomato.scale.genus.filt.20zeros.df %>%
ggplot(aes(x = `1`, y = `2`, fill = Time_Point))+
  geom_point(size=3, shape = 21, color = "black", alpha = 0.9) +
  scale_fill_manual(values = c("sienna1","firebrick3","tomato4"))+
  theme_classic() +
  theme(axis.text = element_text(color = "black")) +
  labs(x=(paste(tomato.round.eigs.genus.20zeros[1], "%")), 
       y=(paste(tomato.round.eigs.genus.20zeros[2], "%")), 
       fill = "Time Point",
       title = "Beta Diversity",
       subtitle = "Genera Level, Tomato Samples Only")

Day 0 Only

# calculate distances
d0.RelAbund.Genus.Filt.zerofilt.dist <- vegdist(d0.RelAbund.Genus.Filt.zerofilt[,-c(1:5)], method = "bray")

# calculate to make PCoA
d0.scale.genus.filt.20zeros <- cmdscale(d0.RelAbund.Genus.Filt.zerofilt.dist, k=2)

# filter metadata
meta.day0 <- subset(AllSamples.Metadata, Time_Point == "Day 0")

# make into data frame and add metadata
d0.scale.genus.filt.20zeros.df <- as.data.frame(cbind(meta.day0, d0.scale.genus.filt.20zeros))

# get eigenvalues
d0.scale.genus.filt.20zeros.eig <- cmdscale(d0.RelAbund.Genus.Filt.zerofilt.dist, k=2, eig = TRUE)
d0.eigs.genus.filt.20zeros <- (100*((d0.scale.genus.filt.20zeros.eig$eig)/(sum(d0.scale.genus.filt.20zeros.eig$eig))))
d0.round.eigs.genus.20zeros <- round(d0.eigs.genus.filt.20zeros, 3)

Plot

d0.scale.genus.filt.20zeros.df %>%
ggplot(aes( x= `1`, y = `2`, fill = Diet))+
  geom_point(size=3, shape = 21, color = "black", alpha = 0.9) +
  scale_fill_manual(values = c("steelblue2", "tomato2")) +
  theme_classic() +
  theme(axis.text = element_text(color = "black")) +
  labs(x=(paste(d0.round.eigs.genus.20zeros[1], "%")), 
       y=(paste(d0.round.eigs.genus.20zeros[2], "%")),
       title = "Beta Diversity",
       subtitle = "Genera Level, Day 0 Only")

Day 7 Only

# calculate distances
d7.RelAbund.Genus.Filt.zerofilt.dist <- vegdist(d7.RelAbund.Genus.Filt.zerofilt[,-c(1:5)], method = "bray")

# calculate to make PCoA
d7.scale.genus.filt.20zeros <- cmdscale(d7.RelAbund.Genus.Filt.zerofilt.dist, k=2)

# filter metadata
meta.day7 <- subset(AllSamples.Metadata, Time_Point == "Day 7")

# make into data frame and add metadata
d7.scale.genus.filt.20zeros.df <- as.data.frame(cbind(meta.day7, d7.scale.genus.filt.20zeros))

# get eigenvalues
d7.scale.genus.filt.20zeros.eig <- cmdscale(d7.RelAbund.Genus.Filt.zerofilt.dist, k=2, eig = TRUE)
d7.eigs.genus.filt.20zeros <- (100*((d7.scale.genus.filt.20zeros.eig$eig)/(sum(d7.scale.genus.filt.20zeros.eig$eig))))
d7.round.eigs.genus.20zeros <- round(d7.eigs.genus.filt.20zeros, 3)

Plot

d7.scale.genus.filt.20zeros.df %>%
ggplot(aes( x= `1`, y = `2`, fill = Diet))+
  geom_point(size=3, shape = 21, color = "black", alpha = 0.9) +
  scale_color_manual(values = c("steelblue2", "tomato2")) +
  theme_classic() +
  theme(axis.text = element_text(color = "black")) +
  labs(x=(paste(d7.round.eigs.genus.20zeros[1], "%")), 
       y=(paste(d7.round.eigs.genus.20zeros[2], "%")),
       title = "Beta Diversity",
       subtitle = "Genera Level, Day 7 Only")

Day 14 Only

# calculate distances
d14.RelAbund.Genus.Filt.zerofilt.dist <- vegdist(d14.RelAbund.Genus.Filt.zerofilt[,-c(1:5)], method = "bray")
# calculate to make PCoA
d14.scale.genus.filt.20zeros <- cmdscale(d14.RelAbund.Genus.Filt.zerofilt.dist, k=2)

# filter metadata
meta.day14 <- subset(AllSamples.Metadata, Time_Point == "Day 14")

# make into data frame and add metadata
d14.scale.genus.filt.20zeros.df <- as.data.frame(cbind(meta.day14, d14.scale.genus.filt.20zeros))

# get eigenvalues
d14.scale.genus.filt.20zeros.eig <- cmdscale(d14.RelAbund.Genus.Filt.zerofilt.dist, k=2, eig = TRUE)
d14.eigs.genus.filt.20zeros <- (100*((d14.scale.genus.filt.20zeros.eig$eig)/(sum(d14.scale.genus.filt.20zeros.eig$eig))))
d14.round.eigs.genus.20zeros <- round(d14.eigs.genus.filt.20zeros, 3)

Plot

d14.scale.genus.filt.20zeros.df %>%
ggplot(aes( x= `1`, y = `2`, fill = Diet))+
  geom_point(size=3, shape = 21, color = "black", alpha = 0.9) +
  scale_color_manual(values = c("steelblue2", "tomato2")) +
  theme_classic() +
  theme(axis.text = element_text(color = "black")) +
  labs(x=(paste(d14.round.eigs.genus.20zeros[1], "%")), 
       y=(paste(d0.round.eigs.genus.20zeros[2], "%")),
       title = "Beta Diversity",
       subtitle = "Genera Level, Day 14 Only")

Alpha Diversity

Wrangling

kable(head(RelAbund.Genus.Filt.zerofilt))
Sample_Name Pig Diet Time_Point Diet_By_Time_Point Abiotrophia Acaryochloris Acetivibrio Acetobacter Acetohalobium Acholeplasma Achromobacter Acidaminococcus Acidilobus Acidimicrobium Acidiphilium Acidithiobacillus Acidobacterium Acidothermus Acidovorax Aciduliprofundum Acinetobacter Actinobacillus Actinomyces Actinosynnema Aerococcus Aeromicrobium Aeromonas Aeropyrum Afipia Aggregatibacter Agrobacterium Ahrensia Ajellomyces Akkermansia Albidiferax Alcanivorax Algoriphagus Alicycliphilus Alicyclobacillus Aliivibrio Alistipes Alkalilimnicola Alkaliphilus Allochromatium Alphatorquevirus Alteromonas Aminobacterium Aminomonas Ammonifex Amycolatopsis Anabaena Anaerobaculum Anaerococcus Anaerofustis Anaeromyxobacter Anaerostipes Anaerotruncus Anaplasma Anoxybacillus Aquifex Arcanobacterium Archaeoglobus Arcobacter Aromatoleum Arthrobacter Arthroderma Arthrospira Aspergillus Asticcacaulis Atopobium Aurantimonas Azoarcus Azorhizobium Azospirillum Azotobacter Babesia Bacillus Bacteroides Bartonella Basfia Bdellovibrio Beggiatoa Beijerinckia Bermanella Beutenbergia Bifidobacterium Blastopirellula Blattabacterium Blautia Bordetella Borrelia Botryotinia Bpp-1-like viruses Brachybacterium Brachyspira Bradyrhizobium Brevibacillus Brevibacterium Brevundimonas Brucella Brugia Buchnera Bulleidia Burkholderia Butyrivibrio Caenorhabditis Caldanaerobacter Caldicellulosiruptor Calditerrivibrio Caldivirga Caminibacter Campylobacter Candida Candidatus Accumulibacter Candidatus Amoebophilus Candidatus Azobacteroides Candidatus Blochmannia Candidatus Cloacamonas Candidatus Desulforudis Candidatus Hamiltonella Candidatus Korarchaeum Candidatus Koribacter Candidatus Liberibacter Candidatus Pelagibacter Candidatus Phytoplasma Candidatus Protochlamydia Candidatus Puniceispirillum Candidatus Regiella Candidatus Riesia Candidatus Solibacter Candidatus Sulcia Capnocytophaga Carboxydothermus Cardiobacterium Carnobacterium Catenibacterium Catenulispora Catonella Caulobacter Cellulomonas Cellulosilyticum Cellvibrio Cenarchaeum Chaetomium Chelativorans Chitinophaga Chlamydia Chlamydomonas Chlamydophila Chlorella Chlorobaculum Chlorobium Chloroflexus Chloroherpeton Chlorovirus Chromobacterium Chromohalobacter Chryseobacterium Chthoniobacter Citreicella Citrobacter Citromicrobium Clavibacter Clavispora Clostridium Coccidioides Collinsella Colwellia Comamonas Conexibacter Congregibacter Coprinopsis Coprobacillus Coprococcus Coprothermobacter Coraliomargarita Corynebacterium Coxiella Croceibacter Crocosphaera Cronobacter Cryptobacterium Cryptosporidium Cupriavidus Cyanidioschyzon Cyanidium Cyanobium Cyanophora Cyanothece Cylindrospermopsis Cytophaga Debaryomyces Dechloromonas Deferribacter Dehalococcoides Dehalogenimonas Deinococcus Delftia Denitrovibrio Dermacoccus Desulfarculus Desulfatibacillum Desulfitobacterium Desulfobacterium Desulfococcus Desulfohalobium Desulfomicrobium Desulfonatronospira Desulfotalea Desulfotomaculum Desulfovibrio Desulfurispirillum Desulfurivibrio Desulfurococcus Desulfuromonas Dethiobacter Dethiosulfovibrio Dialister Dichelobacter Dickeya Dictyoglomus Dictyostelium Dinoroseobacter Dokdonia Dorea Dyadobacter Edwardsiella Eggerthella Ehrlichia Eikenella Elusimicrobium Emericella Emiliania Encephalitozoon Endoriftia Enhydrobacter Entamoeba Enterobacter Enterococcus Enterocytozoon Epsilon15-like viruses Epulopiscium Eremococcus Eremothecium Erwinia Erysipelothrix Erythrobacter Escherichia Ethanoligenens Eubacterium Exiguobacterium Faecalibacterium Ferrimonas Ferroglobus Ferroplasma Fervidobacterium Fibrobacter Filifactor Filobasidiella Finegoldia Flavobacterium Francisella Frankia Fulvimarina Fusobacterium Gallionella Gammaretrovirus Gardnerella Gemella Gemmata Gemmatimonas Geobacillus Geobacter Geodermatophilus Giardia Gibberella Gloeobacter Gluconacetobacter Gluconobacter Gordonia Gracilaria Gramella Granulibacter Granulicatella Guillardia Haemophilus Hahella Halalkalicoccus Halanaerobium Haliangium Haloarcula Halobacterium Haloferax Halogeometricum Halomicrobium Halomonas Haloquadratum Halorhabdus Halorhodospira Halorubrum Haloterrigena Halothermothrix Halothiobacillus Helicobacter Heliobacterium Herbaspirillum Herminiimonas Herpetosiphon Hirschia Histophilus Hoeflea Holdemania Hydrogenivirga Hydrogenobacter Hydrogenobaculum Hyperthermus Hyphomicrobium Hyphomonas Idiomarina Ignicoccus Ignisphaera Ilyobacter Intrasporangium Janibacter Jannaschia Janthinobacterium Jonesia Jonquetella Kangiella Ketogulonicigenium Kineococcus Kingella Klebsiella Kluyveromyces Kocuria Kordia Kosmotoga Kribbella Ktedonobacter Kytococcus L5-like viruses Labrenzia Laccaria Lachancea Lactobacillus Lactococcus Lambda-like viruses Laribacter Lawsonia Leadbetterella Leeuwenhoekiella Legionella Leifsonia Leishmania Lentisphaera Leptosira Leptospira Leptospirillum Leptothrix Leptotrichia Leuconostoc Limnobacter Listeria Loa Lodderomyces Loktanella Lutiella Lyngbya Lysinibacillus Macrococcus Magnaporthe Magnetococcus Magnetospirillum Malassezia Mannheimia Maribacter Maricaulis Marinobacter Marinomonas Mariprofundus Maritimibacter Marivirga Megasphaera Meiothermus Mesoplasma Mesorhizobium Metallosphaera Methanobrevibacter Methanocaldococcus Methanocella Methanococcoides Methanococcus Methanocorpusculum Methanoculleus Methanohalobium Methanohalophilus Methanoplanus Methanopyrus Methanoregula Methanosaeta Methanosarcina Methanosphaera Methanosphaerula Methanospirillum Methanothermobacter Methanothermococcus Methanothermus Methylacidiphilum Methylibium Methylobacillus Methylobacter Methylobacterium Methylocella Methylococcus Methylophaga Methylosinus Methylotenera Methylovorus Meyerozyma Micrococcus Microcoleus Microcystis Micromonas Micromonospora Microscilla Mitsuokella Mobiluncus Moniliophthora Monosiga Moorella Moraxella Moritella Mucilaginibacter Mycobacterium Mycoplasma Myxococcus N4-like viruses Naegleria Nakamurella Nakaseomyces Nanoarchaeum Natranaerobius Natrialba Natronomonas Nautilia Nectria Neisseria Neorickettsia Neosartorya Neptuniibacter Neurospora Nitratiruptor Nitrobacter Nitrococcus Nitrosococcus Nitrosomonas Nitrosopumilus Nitrosospira Nitrospira Nocardia Nocardioides Nocardiopsis Nodularia Nostoc Novosphingobium Oceanibulbus Oceanicaulis Oceanicola Oceanithermus Oceanobacillus Ochrobactrum Octadecabacter Odontella Oenococcus Oligotropha Olsenella Opitutus Oribacterium Orientia Oscillatoria Oscillochloris Ostreococcus Oxalobacter P1-like viruses P2-like viruses P22-like viruses Paenibacillus Paludibacter Pantoea Parabacteroides Parachlamydia Paracoccidioides Paracoccus Paramecium Parascardovia Parvibaculum Parvularcula Pasteurella Paulinella Pectobacterium Pediococcus Pedobacter Pelagibaca Pelobacter Pelodictyon Pelotomaculum Penicillium Peptoniphilus Peptostreptococcus Perkinsus Persephonella Petrotoga Phaeobacter Phaeodactylum Phaeosphaeria Phenylobacterium Phi29-like viruses Photobacterium Photorhabdus Phytophthora Pichia Picrophilus Pirellula Planctomyces Plasmodium Plesiocystis Podospora Polaribacter Polaromonas Polynucleobacter Porphyra Porphyromonas Postia Prevotella Prochlorococcus Propionibacterium Prosthecochloris Proteus Providencia Pseudoalteromonas Pseudomonas Pseudoramibacter Pseudovibrio Psychrobacter Psychroflexus Psychromonas Pyramidobacter Pyrenophora Pyrobaculum Pyrococcus Ralstonia Raphidiopsis Reclinomonas Reinekea Renibacterium Rhizobium Rhodobacter Rhodococcus Rhodomicrobium Rhodomonas Rhodopirellula Rhodopseudomonas Rhodospirillum Rhodothermus Rickettsia Rickettsiella Riemerella Robiginitalea Roseburia Roseibium Roseiflexus Roseobacter Roseomonas Roseovarius Rothia Rubrobacter Ruegeria Ruminococcus SP6-like viruses SPO1-like viruses SPbeta-like viruses Saccharomonospora Saccharomyces Saccharophagus Saccharopolyspora Saccoglossus Sagittula Salinibacter Salinispora Salmonella Sanguibacter Scardovia Scheffersomyces Schizophyllum Schizosaccharomyces Sclerotinia Sebaldella Segniliparus Selenomonas Serratia Shewanella Shigella Shuttleworthia Sideroxydans Simonsiella Sinorhizobium Slackia Sodalis Sorangium Sphaerobacter Sphingobacterium Sphingobium Sphingomonas Sphingopyxis Spirochaeta Spirosoma Stackebrandtia Staphylococcus Staphylothermus Starkeya Stenotrophomonas Stigmatella Streptobacillus Streptococcus Streptomyces Streptosporangium Subdoligranulum Sulfitobacter Sulfolobus Sulfuricurvum Sulfurihydrogenibium Sulfurimonas Sulfurospirillum Sulfurovum Symbiobacterium Synechococcus Synechocystis Syntrophobacter Syntrophomonas Syntrophothermus Syntrophus T4-like viruses T7-like viruses Talaromyces Teredinibacter Terriglobus Tetragenococcus Tetrahymena Thalassiosira Thalassobium Thauera Theileria Thermaerobacter Thermanaerovibrio Thermincola Thermoanaerobacter Thermoanaerobacterium Thermobaculum Thermobifida Thermobispora Thermococcus Thermocrinis Thermodesulfovibrio Thermofilum Thermomicrobium Thermomonospora Thermoplasma Thermoproteus Thermosediminibacter Thermosinus Thermosipho Thermosphaera Thermosynechococcus Thermotoga Thermus Thioalkalivibrio Thiobacillus Thiomicrospira Thiomonas Tolumonas Toxoplasma Treponema Trichodesmium Trichomonas Trichoplax Tropheryma Truepera Trypanosoma Tsukamurella Tuber Turicibacter Uncinocarpus Ureaplasma Ustilago Vanderwaltozyma Variovorax Veillonella Verminephrobacter Verrucomicrobium Verticillium Vibrio Victivallis Volvox Vulcanisaeta Waddlia Weissella Wigglesworthia Wolbachia Wolinella Xanthobacter Xanthomonas Xenorhabdus Xylanimonas Xylella Yarrowia Yersinia Zunongwangia Zygosaccharomyces Zymomonas phiKZ-like viruses unclassified (derived from Actinobacteria (class)) unclassified (derived from Alicyclobacillaceae) unclassified (derived from Alphaproteobacteria) unclassified (derived from Alteromonadales) unclassified (derived from Bacteria) unclassified (derived from Bacteroidetes) unclassified (derived from Betaproteobacteria) unclassified (derived from Burkholderiales) unclassified (derived from Campylobacterales) unclassified (derived from Candidatus Poribacteria) unclassified (derived from Caudovirales) unclassified (derived from Chroococcales) unclassified (derived from Clostridiales Family XI. Incertae Sedis) unclassified (derived from Clostridiales) unclassified (derived from Deltaproteobacteria) unclassified (derived from Elusimicrobia) unclassified (derived from Erysipelotrichaceae) unclassified (derived from Euryarchaeota) unclassified (derived from Flavobacteria) unclassified (derived from Flavobacteriaceae) unclassified (derived from Flavobacteriales) unclassified (derived from Gammaproteobacteria) unclassified (derived from Lachnospiraceae) unclassified (derived from Methylophilales) unclassified (derived from Myoviridae) unclassified (derived from Opitutaceae) unclassified (derived from Podoviridae) unclassified (derived from Rhodobacteraceae) unclassified (derived from Rhodobacterales) unclassified (derived from Rickettsiales) unclassified (derived from Ruminococcaceae) unclassified (derived from Siphoviridae) unclassified (derived from Thermotogales) unclassified (derived from Verrucomicrobia subdivision 3) unclassified (derived from Verrucomicrobiales) unclassified (derived from Vibrionaceae) unclassified (derived from Vibrionales) unclassified (derived from Viruses) unclassified (derived from other sequences)
ShotgunWGS-ControlPig6GutMicrobiome-Day14 6 Control Day 14 Control Day 14 0.0013057 6.98e-05 0.0005123 1.70e-05 0.0002670 0.0002007 0.0000495 0.0129311 2.6e-06 1.52e-05 6.29e-05 6.31e-05 0.0000941 0.0001002 0.0002036 0.0000559 0.0002046 0.0006754 0.0002587 4.97e-05 0.0000879 6.40e-06 0.0002193 8.0e-06 5.90e-06 0.0000948 0.0001332 4.10e-06 2.8e-06 0.0003798 0.0000881 0.0000657 0.0000441 2.09e-05 0.0003706 0.0001340 0.0005110 0.0001064 0.0033871 5.10e-05 2.10e-06 0.0000381 0.0001312 2.60e-05 0.0002358 4.15e-05 0.0001219 3.35e-05 0.0011854 0.0003992 0.0003378 0.0011052 0.0017118 5.70e-06 0.0003584 0.0001428 0.0001368 0.0001188 0.0001322 0.0001062 0.0002451 3.6e-06 2.37e-05 4.30e-05 7.40e-05 0.0091799 2.89e-05 0.0000737 4.90e-05 4.25e-05 4.95e-05 2.1e-06 0.0070241 0.0821852 5.72e-05 0.0001917 0.0000861 2.09e-05 2.47e-05 7.00e-06 0.0001046 0.0105635 0.0000760 1.34e-05 0.0045629 0.0002440 0.0000750 4.90e-06 1.19e-05 0.0000776 0.0013351 0.0001835 0.0004561 0.0000719 3.25e-05 0.0000711 9.00e-06 2.22e-05 0.0005981 0.0006347 0.0103885 3.48e-05 0.0017562 0.0022862 0.0000760 7.20e-06 8.50e-06 0.0011851 1.08e-05 0.0000523 5.05e-05 0.0001809 1.31e-05 1.62e-05 0.0003760 1.24e-05 1.93e-05 0.0001729 5.9e-06 1.57e-05 4.35e-05 4.56e-05 1.19e-05 4.60e-06 1.8e-06 0.0004154 3.60e-06 0.0005886 0.0011439 1.86e-05 5.98e-05 0.0012611 6.44e-05 4.56e-05 0.0002100 0.0000750 0.0006844 0.0001497 3.90e-06 7.7e-06 0.0000706 0.0005677 3.53e-05 3.27e-05 3.48e-05 0.0e+00 0.0001706 0.0006079 0.0002337 0.0001206 1.0e-06 0.0001036 0.0000902 0.0000845 2.19e-05 3.30e-06 0.0001175 4.10e-06 0.0001051 4.40e-06 0.0767302 3.10e-06 0.0049257 0.0000662 0.0000307 8.35e-05 3.68e-05 6.70e-06 0.0003621 0.0063405 0.0000866 0.0000523 0.0017554 5.00e-05 0.0001224 6.31e-05 0.0000956 0.0010161 6.70e-06 0.0002183 2.1e-06 6.4e-06 0.0000031 1.3e-06 0.0004015 1.42e-05 0.0004213 8.2e-06 0.0000923 0.0001430 0.0009210 0.0000850 0.0002654 0.0000569 0.0001660 8.00e-06 0.0000796 0.0001523 0.0037569 0.0001355 0.0001580 0.0000680 0.0001482 1.80e-05 0.0001946 0.0029103 0.0012480 8.53e-05 5.82e-05 4.40e-06 0.0001660 0.0001098 0.0002556 0.0062201 8.50e-05 0.0001167 0.0003136 4.51e-05 3.50e-05 0.0000943 0.0052538 0.0004803 0.0000763 0.0027436 1.65e-05 7.20e-06 0.0000995 1.39e-05 0.0e+00 5.0e-07 3.30e-06 8.20e-06 0.0000255 0.0001585 0.0027601 1.21e-05 1.0e-06 0.0001392 2.99e-05 9.30e-06 0.0000727 0.0000740 0.0000858 0.0005095 0.0045129 0.0713607 0.0005494 0.0345703 0.0000340 3.25e-05 2.45e-05 0.0002023 0.0012287 0.0001531 1.88e-05 0.0007628 0.0012769 0.0001080 0.0002448 1.03e-05 0.0018682 3.38e-05 3.0e-07 0.0006563 4.02e-05 2.37e-05 0.0000544 0.0018128 0.0014026 5.33e-05 6.20e-06 2.47e-05 0.0001211 2.89e-05 6.36e-05 3.22e-05 5.0e-07 0.0003646 4.17e-05 0.0001232 7.2e-06 0.0003079 0.0001116 1.11e-05 0.0002358 0.0000729 2.24e-05 1.65e-05 1.34e-05 1.57e-05 1.00e-05 3.12e-05 1.52e-05 1.49e-05 5.82e-05 1.06e-05 1.96e-05 0.0005736 2.53e-05 0.0005641 0.0013552 3.79e-05 0.0000572 0.0001337 3.14e-05 0.0001221 8.00e-06 0.0034288 1.78e-05 3.66e-05 6.78e-05 1.11e-05 2.01e-05 4.30e-05 0.0000742 1.11e-05 6.7e-06 0.0006200 3.84e-05 2.86e-05 3.30e-05 0.0000580 0.0000861 0.0000698 2.81e-05 1.75e-05 0.0000925 8.80e-06 0.0002023 9.80e-06 0.0000673 2.96e-05 0.0000956 5.57e-05 3.61e-05 4.17e-05 3.0e-07 1.57e-05 3.3e-06 7.5e-06 0.0460339 0.0006994 0.0000167 3.74e-05 0.0000863 0.0003785 0.0003412 0.0001433 0.0000557 2.19e-05 2.63e-05 5e-07 0.0001330 3.0e-07 0.0000559 0.0006277 0.0004314 8.00e-06 0.0011256 2.10e-06 3.3e-06 1.08e-05 2.09e-05 1.86e-05 0.0002981 0.0001296 1.49e-05 0.0001492 0.0001649 6.70e-06 0.0000982 0.0002020 3.53e-05 0.0001242 0.0001456 1.21e-05 2.27e-05 0.0001381 0.0101970 0.0001415 0.0000500 0.0000794 1.11e-05 0.0004927 0.0001075 4.61e-05 0.0001031 0.0003092 0.0003340 0.0000987 3.30e-05 3.50e-05 0.0000711 2.71e-05 7.32e-05 0.0000693 0.0004033 0.0001286 5.49e-05 0.0001090 0.0000902 2.60e-06 1.16e-05 2.63e-05 0.0000466 5.26e-05 1.03e-05 0.0001515 5.10e-05 0.0000964 3.30e-06 6.20e-06 3.63e-05 4.46e-05 6.2e-06 5.67e-05 1.88e-05 8.86e-05 1.24e-05 5.64e-05 0.0000796 0.0274527 0.0015438 3.9e-06 1.47e-05 0.0011094 2.37e-05 0.0000129 0.0001693 0.0004409 0.0003685 0.0001484 5.0e-07 8.20e-06 5.67e-05 1.83e-05 1.0e-06 0.0004064 8.20e-06 1.60e-05 4.07e-05 6.2e-06 0.0002317 1.42e-05 3.99e-05 9.80e-06 2.47e-05 0.0001008 0.0000835 3.12e-05 0.0001214 0.0001090 1.06e-05 6.60e-05 4.92e-05 0.0000941 0.0001098 4.59e-05 1.78e-05 0.0001775 0.0000698 5.4e-06 2.53e-05 2.94e-05 4.07e-05 0.0004453 0.0000706 1.37e-05 2.1e-06 0.0002000 3.71e-05 0.0066190 0.0002461 0.0050074 1.16e-05 1.08e-05 2.04e-05 2.37e-05 0.0001325 0.0000000 3.0e-07 0.0e+00 0.0020159 0.0012415 0.0001355 0.0063554 8.20e-06 1.0e-06 8.68e-05 1.08e-05 0.0002180 6.62e-05 2.19e-05 0.0001057 1.3e-06 0.0001788 0.0004473 0.0008558 6.40e-06 0.0006842 0.0003234 0.0011539 1.29e-05 0.0004218 0.0003533 8.20e-06 5.72e-05 0.0001745 5.20e-06 9.30e-06 1.00e-05 4.84e-05 0.0e+00 0.0002054 0.0000863 1.62e-05 2.1e-06 1.47e-05 4.07e-05 0.0000966 0.0000356 3.04e-05 4.4e-06 0.0002322 0.0001095 0.0000495 5.4e-06 0.0022865 1.3e-06 0.2653352 0.0001701 0.0002247 3.74e-05 0.0000853 0.0000304 0.0001982 0.0007669 0.0014498 1.29e-05 0.0001301 4.90e-05 0.0001294 0.0004409 4.60e-06 2.50e-05 0.0001142 0.0001497 6.4e-06 1.5e-06 5.23e-05 3.63e-05 0.0002098 0.0001556 0.0001904 2.14e-05 1.0e-06 0.0001554 0.0002415 0.0001373 0.0001670 5.54e-05 2.30e-06 0.0001265 0.0001886 0.0240077 4.40e-06 0.0003873 0.0000938 9.80e-06 3.50e-05 0.0000856 0.0002105 0.0000768 0.0373864 1.3e-06 9.00e-06 2.8e-06 3.43e-05 1.39e-05 0.0001763 0.0001085 6.7e-06 7.70e-06 0.0001175 0.0001028 0.0001987 0.0001417 0.0001647 1.26e-05 8.8e-06 1.47e-05 3.6e-06 0.0006641 8.80e-06 0.0093160 0.0001337 0.0007009 0.0000611 0.0013000 5.44e-05 1.29e-05 0.0001417 0.0034399 2.60e-05 0.0001118 0.0001392 0.0001142 2.11e-05 7.47e-05 6.78e-05 0.0006087 0.0006646 4.59e-05 0.0011864 7.70e-06 2.53e-05 0.0000636 3.30e-05 0.0001755 0.0284657 0.0004780 0.0000657 0.0212210 1.24e-05 4.07e-05 3.61e-05 0.0001567 0.0000925 7.06e-05 5.98e-05 0.0009068 0.0005893 0.0001281 0.0002147 0.0009277 0.0003136 0.0002525 5.70e-06 0.0e+00 1.19e-05 4.43e-05 5.82e-05 2.8e-06 8.80e-06 1.57e-05 5.4e-06 4.25e-05 4.1e-06 0.0002438 0.0001904 0.0007352 0.0016657 0.0009148 0.0001428 0.0001144 5.75e-05 0.0001263 2.45e-05 0.0000925 2.19e-05 6.44e-05 5.10e-05 4.69e-05 1.5e-06 0.0004958 0.0006368 0.0002332 3.6e-06 0.0001193 0.0005569 0.0001469 0.0000801 0.0000760 0.0000613 2.55e-05 0.0000902 1.11e-05 0.0008166 0.0001051 0.0001180 1.75e-05 1.47e-05 4.48e-05 1.19e-05 3.61e-05 2.1e-06 0.0001995 2.3e-06 4.95e-05 1.24e-05 2.80e-06 0.0000472 0.0061611 0.0000701 1.67e-05 1.8e-06 0.0005584 0.0001708 2.53e-05 4.4e-06 1.83e-05 4.48e-05 2.60e-06 2.94e-05 0.0001108 6.11e-05 0.0001958 0.0000356 0.0001737 0.0000636 1.34e-05 0.0003090 0.0003956 2.3e-06 5.13e-05 1.0e-06 2.65e-05 0.0003023 1.44e-05 8.20e-06 0.0001495 0.0002438 2.8e-06 0.0000941 1.24e-05 6.70e-06 1.65e-05 8.50e-06 0.0001085 0.0032562 4.79e-05 6.34e-05 0.0045420 0.0000876 0.0001551 0.0001721 0.0000673 0.0001054 0.0042367 2.80e-06 0.0000531 0.0000222 1.80e-06 1.00e-05 1.00e-05 4.1e-06 0.0041192 0.0001979 2.96e-05 0.0000309 3.07e-05 1.44e-05 8.50e-06 6.49e-05 1.24e-05
ShotgunWGS-ControlPig8GutMicrobiome-Day0 8 Control Day 0 Control Day 0 0.0013478 9.90e-05 0.0007097 2.05e-05 0.0003269 0.0003021 0.0000709 0.0095018 4.8e-06 3.00e-05 8.19e-05 8.93e-05 0.0001283 0.0001319 0.0003576 0.0001186 0.0002488 0.0005859 0.0003412 6.69e-05 0.0000843 1.62e-05 0.0002676 7.9e-06 5.00e-06 0.0000990 0.0001931 1.07e-05 2.9e-06 0.0005195 0.0001507 0.0000793 0.0001486 3.55e-05 0.0004369 0.0001676 0.0014026 0.0000802 0.0046520 5.24e-05 4.30e-06 0.0000683 0.0002467 4.43e-05 0.0002855 5.26e-05 0.0001764 5.93e-05 0.0014702 0.0005607 0.0004626 0.0013019 0.0031158 1.14e-05 0.0004486 0.0001605 0.0002319 0.0001286 0.0001686 0.0001250 0.0003574 4.3e-06 3.00e-05 3.45e-05 7.79e-05 0.0101694 3.55e-05 0.0001119 5.95e-05 7.02e-05 7.14e-05 3.3e-06 0.0086887 0.1052813 8.05e-05 0.0002052 0.0001057 2.71e-05 3.90e-05 1.38e-05 0.0001431 0.0228505 0.0001224 3.95e-05 0.0056276 0.0002545 0.0001212 9.50e-06 3.30e-06 0.0001017 0.0015971 0.0002467 0.0005343 0.0000993 4.07e-05 0.0000938 1.12e-05 2.29e-05 0.0005540 0.0008445 0.0095013 4.86e-05 0.0021813 0.0028261 0.0001071 1.26e-05 1.57e-05 0.0020271 1.76e-05 0.0000609 8.98e-05 0.0004119 1.64e-05 3.81e-05 0.0004647 1.36e-05 2.31e-05 0.0002140 4.3e-06 1.88e-05 6.05e-05 6.76e-05 1.67e-05 5.70e-06 2.0e-07 0.0004440 1.17e-05 0.0009138 0.0012814 2.12e-05 8.05e-05 0.0018825 8.79e-05 6.48e-05 0.0002269 0.0000983 0.0009695 0.0001395 6.20e-06 4.0e-06 0.0001088 0.0008223 3.05e-05 3.57e-05 5.19e-05 1.4e-06 0.0002633 0.0006652 0.0002636 0.0001545 3.3e-06 0.0001426 0.0001190 0.0001619 8.93e-05 1.12e-05 0.0001638 7.60e-06 0.0001467 6.00e-06 0.1009617 1.31e-05 0.0048772 0.0000931 0.0000595 9.81e-05 4.02e-05 1.02e-05 0.0005309 0.0063733 0.0001293 0.0001126 0.0009138 5.43e-05 0.0002088 5.71e-05 0.0001119 0.0013080 1.76e-05 0.0002993 2.1e-06 4.3e-06 0.0000912 3.3e-06 0.0004788 1.33e-05 0.0006181 8.3e-06 0.0002207 0.0001400 0.0008804 0.0000902 0.0003302 0.0000831 0.0002276 1.33e-05 0.0001038 0.0002336 0.0048170 0.0001771 0.0002169 0.0000943 0.0001814 2.95e-05 0.0002493 0.0030973 0.0022225 9.90e-05 9.40e-05 8.30e-06 0.0001840 0.0001664 0.0003886 0.0024751 7.62e-05 0.0001355 0.0003700 8.93e-05 4.60e-05 0.0001848 0.0149870 0.0006012 0.0000940 0.0030911 2.45e-05 1.33e-05 0.0001614 1.48e-05 1.0e-06 2.6e-06 1.00e-05 1.60e-05 0.0003017 0.0001817 0.0035749 1.81e-05 2.1e-06 0.0002136 5.48e-05 1.00e-05 0.0000893 0.0001038 0.0001074 0.0010485 0.0059181 0.0601988 0.0006819 0.0296095 0.0000488 2.57e-05 2.71e-05 0.0002305 0.0016578 0.0002571 2.79e-05 0.0007926 0.0017283 0.0001786 0.0003407 9.30e-06 0.0025525 4.67e-05 2.0e-07 0.0012178 5.79e-05 4.26e-05 0.0000809 0.0021025 0.0016695 6.55e-05 1.40e-05 4.14e-05 0.0001609 5.26e-05 6.52e-05 5.88e-05 7.0e-07 0.0005845 5.36e-05 0.0001469 7.4e-06 0.0003728 0.0001350 1.45e-05 0.0002857 0.0000921 2.71e-05 2.14e-05 1.43e-05 2.29e-05 1.57e-05 4.10e-05 2.60e-05 2.14e-05 7.33e-05 1.19e-05 2.24e-05 0.0006481 3.71e-05 0.0008431 0.0014135 4.31e-05 0.0000831 0.0001567 2.79e-05 0.0001729 1.07e-05 0.0043720 2.29e-05 4.02e-05 7.74e-05 1.10e-05 3.17e-05 6.07e-05 0.0001157 1.43e-05 5.7e-06 0.0007131 3.55e-05 5.31e-05 4.81e-05 0.0000883 0.0001131 0.0000890 3.86e-05 2.88e-05 0.0001488 1.64e-05 0.0002228 1.71e-05 0.0000693 7.19e-05 0.0001533 8.24e-05 5.76e-05 5.71e-05 0.0e+00 3.02e-05 1.0e-05 7.9e-06 0.0365643 0.0006574 0.0000248 4.60e-05 0.0001417 0.0004093 0.0004226 0.0001619 0.0000962 3.05e-05 6.48e-05 2e-07 0.0001926 2.0e-07 0.0000995 0.0007257 0.0004550 1.76e-05 0.0013728 3.30e-06 6.0e-06 1.90e-05 2.57e-05 2.69e-05 0.0003719 0.0001431 2.17e-05 0.0001840 0.0001967 8.30e-06 0.0000921 0.0003433 6.14e-05 0.0001674 0.0001752 2.02e-05 3.14e-05 0.0002436 0.0059802 0.0002057 0.0000633 0.0001245 1.36e-05 0.0006671 0.0001657 5.26e-05 0.0001343 0.0004228 0.0004162 0.0001257 3.69e-05 5.52e-05 0.0001081 4.19e-05 8.33e-05 0.0000955 0.0005721 0.0001462 5.29e-05 0.0001212 0.0001283 6.40e-06 2.33e-05 4.93e-05 0.0001126 8.43e-05 2.00e-05 0.0002076 4.10e-05 0.0001271 1.17e-05 2.24e-05 5.50e-05 3.81e-05 7.6e-06 6.79e-05 3.38e-05 9.76e-05 1.76e-05 7.88e-05 0.0001602 0.0218968 0.0024935 5.5e-06 2.10e-05 0.0011928 2.64e-05 0.0000190 0.0002362 0.0005878 0.0004047 0.0002167 3.6e-06 2.67e-05 7.76e-05 1.93e-05 1.2e-06 0.0005021 1.74e-05 2.36e-05 5.26e-05 8.3e-06 0.0003097 1.50e-05 4.31e-05 1.79e-05 2.31e-05 0.0001198 0.0001093 4.24e-05 0.0001821 0.0001531 1.29e-05 8.31e-05 7.31e-05 0.0001100 0.0002026 6.57e-05 1.98e-05 0.0002267 0.0000931 7.1e-06 3.21e-05 3.79e-05 5.79e-05 0.0005259 0.0001162 1.64e-05 7.0e-07 0.0002638 2.43e-05 0.0072738 0.0003702 0.0017714 1.93e-05 2.26e-05 3.40e-05 3.83e-05 0.0001764 0.0000005 3.8e-06 4.5e-06 0.0027108 0.0021187 0.0001431 0.0093044 1.12e-05 1.7e-06 8.05e-05 1.69e-05 0.0002426 8.71e-05 3.36e-05 0.0001257 3.6e-06 0.0002074 0.0005376 0.0013526 8.30e-06 0.0008312 0.0003002 0.0013007 1.48e-05 0.0006295 0.0004781 1.02e-05 6.57e-05 0.0002369 6.20e-06 1.98e-05 1.29e-05 4.12e-05 1.9e-06 0.0002690 0.0001145 3.71e-05 5.5e-06 2.60e-05 6.90e-05 0.0001586 0.0000562 3.86e-05 9.5e-06 0.0003917 0.0002409 0.0000940 5.5e-06 0.0039265 1.7e-06 0.1953868 0.0002638 0.0002686 6.26e-05 0.0001017 0.0000571 0.0002417 0.0009345 0.0005771 1.76e-05 0.0001795 7.81e-05 0.0001679 0.0007288 6.20e-06 3.19e-05 0.0001490 0.0001812 5.7e-06 5.0e-07 8.31e-05 4.86e-05 0.0002498 0.0001893 0.0002359 3.60e-05 7.0e-07 0.0002340 0.0003250 0.0001838 0.0002314 7.29e-05 2.40e-06 0.0002000 0.0002702 0.0171608 9.30e-06 0.0005097 0.0001350 2.52e-05 5.79e-05 0.0000945 0.0002457 0.0001267 0.0555783 0.0e+00 1.00e-05 2.6e-06 4.86e-05 2.57e-05 0.0002005 0.0001519 4.3e-06 1.26e-05 0.0001588 0.0001309 0.0002948 0.0001664 0.0002036 2.07e-05 9.5e-06 3.90e-05 2.9e-06 0.0008007 1.62e-05 0.0080618 0.0001619 0.0009002 0.0001214 0.0010104 4.19e-05 2.19e-05 0.0002159 0.0041267 4.00e-05 0.0001348 0.0002038 0.0002421 4.48e-05 8.76e-05 7.76e-05 0.0009776 0.0008926 6.52e-05 0.0013411 1.38e-05 4.24e-05 0.0001002 4.90e-05 0.0002655 0.0077406 0.0007007 0.0001212 0.0155818 2.43e-05 5.05e-05 4.90e-05 0.0001390 0.0001029 8.17e-05 9.93e-05 0.0010376 0.0010085 0.0001517 0.0002898 0.0011535 0.0003964 0.0003407 2.02e-05 8.3e-06 6.40e-06 5.93e-05 9.86e-05 4.5e-06 1.33e-05 2.33e-05 6.9e-06 5.79e-05 4.5e-06 0.0003150 0.0002264 0.0008414 0.0019737 0.0009819 0.0001759 0.0001381 8.05e-05 0.0001938 3.10e-05 0.0001164 2.88e-05 9.24e-05 7.69e-05 7.14e-05 1.7e-06 0.0006600 0.0011904 0.0002948 2.9e-06 0.0001426 0.0007307 0.0001933 0.0001059 0.0001067 0.0000852 3.17e-05 0.0001143 9.80e-06 0.0012692 0.0001229 0.0003755 1.62e-05 2.14e-05 6.67e-05 2.88e-05 3.50e-05 6.4e-06 0.0002702 5.5e-06 5.74e-05 2.33e-05 5.50e-06 0.0000769 0.0047134 0.0001209 7.21e-05 5.7e-06 0.0007326 0.0003717 3.95e-05 7.1e-06 3.62e-05 5.05e-05 3.60e-06 4.76e-05 0.0001538 7.59e-05 0.0002807 0.0000402 0.0001895 0.0000779 2.60e-05 0.0003219 0.0004664 6.4e-06 6.40e-05 1.4e-06 4.98e-05 0.0003367 3.36e-05 1.05e-05 0.0001955 0.0005247 4.0e-06 0.0001940 1.71e-05 1.17e-05 4.07e-05 1.12e-05 0.0001238 0.0040044 8.45e-05 7.83e-05 0.0067576 0.0001309 0.0002533 0.0002098 0.0001726 0.0001759 0.0050081 3.80e-06 0.0001398 0.0001421 7.90e-06 2.02e-05 2.26e-05 2.9e-06 0.0122500 0.0002545 4.55e-05 0.0000800 6.05e-05 1.90e-05 7.90e-06 7.40e-05 7.90e-06
ShotgunWGS-ControlPig3GutMicrobiome-Day14 3 Control Day 14 Control Day 14 0.0010663 6.91e-05 0.0005364 1.55e-05 0.0002629 0.0002116 0.0000510 0.0082861 6.5e-06 2.10e-05 6.29e-05 5.75e-05 0.0000800 0.0000780 0.0002916 0.0000635 0.0001691 0.0004657 0.0001808 4.69e-05 0.0001909 4.70e-06 0.0001973 6.7e-06 3.10e-06 0.0000748 0.0001147 3.40e-06 2.8e-06 0.0003675 0.0001202 0.0000782 0.0000704 3.65e-05 0.0003546 0.0001259 0.0007555 0.0000842 0.0034668 4.48e-05 1.30e-06 0.0000505 0.0001572 2.90e-05 0.0002165 5.05e-05 0.0001256 3.81e-05 0.0010849 0.0003916 0.0003149 0.0010981 0.0022428 1.14e-05 0.0003667 0.0001215 0.0001254 0.0001054 0.0001474 0.0000995 0.0002015 3.6e-06 2.30e-05 2.87e-05 5.52e-05 0.0051601 2.18e-05 0.0000875 4.77e-05 6.16e-05 3.96e-05 1.6e-06 0.0067811 0.0763643 4.40e-05 0.0001453 0.0000816 1.29e-05 2.49e-05 9.80e-06 0.0000575 0.0065677 0.0000738 2.33e-05 0.0048146 0.0002178 0.0000637 5.40e-06 3.10e-06 0.0000578 0.0012535 0.0001707 0.0004266 0.0000531 2.98e-05 0.0000583 7.80e-06 1.97e-05 0.0003965 0.0006247 0.0089022 2.69e-05 0.0017150 0.0024133 0.0000720 5.40e-06 9.60e-06 0.0012351 1.42e-05 0.0000544 5.59e-05 0.0002046 8.00e-06 2.25e-05 0.0003934 1.01e-05 1.35e-05 0.0001797 2.6e-06 2.07e-05 3.88e-05 4.74e-05 1.68e-05 2.80e-06 5.0e-07 0.0003660 8.00e-06 0.0006288 0.0010722 1.79e-05 6.99e-05 0.0009637 5.39e-05 6.37e-05 0.0002082 0.0000526 0.0007143 0.0001274 5.40e-06 8.0e-06 0.0000800 0.0005915 1.94e-05 1.89e-05 4.22e-05 8.0e-07 0.0001784 0.0005144 0.0002326 0.0001580 1.8e-06 0.0000969 0.0000870 0.0000873 4.35e-05 8.00e-06 0.0001217 7.00e-06 0.0000868 1.01e-05 0.0774401 4.70e-06 0.0053002 0.0000837 0.0000438 7.59e-05 3.03e-05 3.60e-06 0.0003274 0.0064006 0.0000922 0.0000640 0.0004708 3.76e-05 0.0001127 4.87e-05 0.0000834 0.0008477 7.50e-06 0.0002261 1.6e-06 4.1e-06 0.0001197 2.6e-06 0.0004048 1.22e-05 0.0004131 3.9e-06 0.0000956 0.0000987 0.0008091 0.0000811 0.0002502 0.0000637 0.0001968 7.30e-06 0.0000774 0.0001564 0.0038092 0.0001290 0.0001515 0.0000875 0.0001367 1.84e-05 0.0002129 0.0026608 0.0014037 7.02e-05 6.14e-05 5.70e-06 0.0001525 0.0001274 0.0002318 0.0019038 6.32e-05 0.0001140 0.0002872 3.96e-05 3.06e-05 0.0001072 0.0083552 0.0004289 0.0000616 0.0024832 1.53e-05 9.30e-06 0.0001171 1.01e-05 1.0e-06 5.0e-07 3.60e-06 1.37e-05 0.0000293 0.0001238 0.0034243 1.40e-05 1.0e-06 0.0001512 2.67e-05 8.50e-06 0.0000790 0.0000541 0.0000800 0.0007941 0.0051174 0.0696863 0.0005392 0.0454851 0.0000368 2.07e-05 1.53e-05 0.0002056 0.0012911 0.0001608 1.92e-05 0.0006146 0.0012753 0.0001318 0.0002471 6.70e-06 0.0017461 4.17e-05 5.0e-07 0.0004649 4.45e-05 2.07e-05 0.0000624 0.0016751 0.0013395 4.58e-05 6.20e-06 2.69e-05 0.0001388 3.55e-05 4.40e-05 2.67e-05 1.3e-06 0.0003551 4.56e-05 0.0001699 6.5e-06 0.0002611 0.0000956 1.14e-05 0.0002357 0.0000710 2.23e-05 1.19e-05 9.60e-06 1.48e-05 9.10e-06 2.67e-05 2.02e-05 1.06e-05 5.23e-05 1.27e-05 1.94e-05 0.0005309 2.77e-05 0.0005602 0.0013338 5.34e-05 0.0000699 0.0001352 2.54e-05 0.0001479 7.00e-06 0.0032586 2.28e-05 2.56e-05 4.71e-05 9.10e-06 1.97e-05 5.13e-05 0.0000844 9.10e-06 4.1e-06 0.0005457 2.49e-05 3.03e-05 3.70e-05 0.0000767 0.0000629 0.0000546 2.72e-05 2.15e-05 0.0000699 1.14e-05 0.0001554 9.10e-06 0.0000401 4.33e-05 0.0000963 4.79e-05 3.24e-05 3.16e-05 3.0e-07 1.58e-05 3.6e-06 4.1e-06 0.0424407 0.0011250 0.0000127 4.14e-05 0.0000963 0.0003903 0.0002950 0.0001254 0.0000484 1.58e-05 3.99e-05 3e-07 0.0001375 8.0e-07 0.0000697 0.0006397 0.0005550 1.58e-05 0.0011362 3.10e-06 4.4e-06 1.61e-05 1.63e-05 2.43e-05 0.0003113 0.0001347 1.68e-05 0.0001375 0.0001279 5.20e-06 0.0000699 0.0001976 3.91e-05 0.0001217 0.0001199 1.55e-05 2.90e-05 0.0001404 0.0070940 0.0001349 0.0000502 0.0000805 8.80e-06 0.0005001 0.0001083 3.78e-05 0.0001152 0.0003175 0.0002989 0.0001114 2.67e-05 3.03e-05 0.0000831 3.13e-05 6.40e-05 0.0000694 0.0003955 0.0000984 4.43e-05 0.0000966 0.0000888 9.80e-06 1.40e-05 3.57e-05 0.0000736 5.90e-05 1.58e-05 0.0001634 4.20e-05 0.0000974 8.30e-06 1.99e-05 3.52e-05 2.75e-05 6.0e-06 4.58e-05 1.89e-05 9.74e-05 1.45e-05 5.75e-05 0.0000901 0.0148157 0.0003079 3.1e-06 1.42e-05 0.0010292 2.05e-05 0.0000104 0.0001142 0.0003781 0.0003056 0.0001492 5.0e-07 9.60e-06 4.97e-05 1.50e-05 1.0e-06 0.0003986 1.32e-05 1.76e-05 4.12e-05 5.4e-06 0.0002108 8.30e-06 2.93e-05 9.30e-06 1.24e-05 0.0000769 0.0000919 3.26e-05 0.0001248 0.0001051 9.80e-06 6.24e-05 5.49e-05 0.0000704 0.0001028 4.09e-05 1.63e-05 0.0001816 0.0000948 5.2e-06 1.76e-05 2.95e-05 3.96e-05 0.0004079 0.0000570 1.35e-05 1.8e-06 0.0002269 1.92e-05 0.0035111 0.0003020 0.0020639 2.02e-05 1.86e-05 2.49e-05 2.90e-05 0.0001507 0.0000018 8.0e-07 3.0e-07 0.0020722 0.0013423 0.0001046 0.0061947 5.70e-06 8.0e-07 5.26e-05 1.04e-05 0.0000956 6.68e-05 2.95e-05 0.0000710 7.8e-06 0.0001577 0.0004610 0.0008676 5.40e-06 0.0007184 0.0002629 0.0010857 8.50e-06 0.0003854 0.0003142 4.90e-06 5.67e-05 0.0001709 4.70e-06 1.27e-05 5.70e-06 4.69e-05 3.0e-07 0.0002201 0.0000746 1.42e-05 3.4e-06 2.15e-05 5.93e-05 0.0001028 0.0000287 2.54e-05 5.2e-06 0.0002346 0.0002020 0.0000702 8.3e-06 0.0020786 1.3e-06 0.2305057 0.0002497 0.0001601 4.38e-05 0.0000710 0.0000381 0.0001836 0.0006775 0.0005781 1.04e-05 0.0001204 4.38e-05 0.0001347 0.0003996 2.30e-06 3.08e-05 0.0001083 0.0001492 5.2e-06 5.0e-07 5.44e-05 2.46e-05 0.0001771 0.0001494 0.0001683 2.28e-05 1.8e-06 0.0001500 0.0002357 0.0001533 0.0001634 5.78e-05 1.80e-06 0.0001173 0.0002080 0.0215569 6.20e-06 0.0004234 0.0000901 1.61e-05 3.26e-05 0.0000883 0.0001805 0.0000976 0.0341373 1.3e-06 8.50e-06 3.4e-06 2.90e-05 1.86e-05 0.0002256 0.0000862 4.1e-06 7.30e-06 0.0001202 0.0000982 0.0001968 0.0001013 0.0000974 8.50e-06 8.0e-06 1.61e-05 3.4e-06 0.0005734 1.09e-05 0.0096929 0.0001329 0.0006918 0.0001075 0.0009518 3.60e-05 2.93e-05 0.0001518 0.0028976 3.16e-05 0.0001096 0.0001500 0.0001334 2.80e-05 8.21e-05 6.45e-05 0.0005936 0.0006374 3.65e-05 0.0011680 1.32e-05 2.56e-05 0.0000557 3.52e-05 0.0002131 0.1117937 0.0004628 0.0000761 0.0196324 1.97e-05 2.87e-05 3.65e-05 0.0001295 0.0000914 6.68e-05 6.89e-05 0.0008067 0.0010129 0.0001285 0.0001942 0.0009562 0.0003017 0.0002357 9.30e-06 5.2e-06 2.30e-06 3.96e-05 6.40e-05 6.2e-06 8.30e-06 1.89e-05 3.4e-06 4.20e-05 7.8e-06 0.0002497 0.0001639 0.0006892 0.0015045 0.0007671 0.0001254 0.0000919 4.58e-05 0.0001378 2.20e-05 0.0000901 2.41e-05 7.87e-05 5.65e-05 4.53e-05 2.1e-06 0.0005076 0.0009085 0.0002375 6.5e-06 0.0001228 0.0005633 0.0001331 0.0000697 0.0000839 0.0000647 2.95e-05 0.0000738 3.60e-06 0.0007803 0.0000927 0.0001207 8.00e-06 1.04e-05 4.33e-05 1.68e-05 2.41e-05 4.9e-06 0.0001660 2.6e-06 3.94e-05 7.30e-06 2.30e-06 0.0000756 0.0044696 0.0000826 3.63e-05 3.4e-06 0.0005467 0.0001606 2.82e-05 4.4e-06 2.87e-05 5.67e-05 3.90e-06 3.34e-05 0.0000953 6.24e-05 0.0001994 0.0000334 0.0001158 0.0000552 1.09e-05 0.0002763 0.0003709 3.1e-06 6.37e-05 1.8e-06 2.69e-05 0.0002730 2.10e-05 7.50e-06 0.0001494 0.0002119 1.3e-06 0.0001740 1.06e-05 5.40e-06 1.63e-05 9.60e-06 0.0000826 0.0029442 5.36e-05 6.40e-05 0.0049519 0.0000805 0.0001564 0.0001533 0.0000894 0.0001339 0.0043629 4.10e-06 0.0000554 0.0000422 4.90e-06 1.63e-05 1.24e-05 2.3e-06 0.0055364 0.0002965 3.47e-05 0.0000412 3.57e-05 8.80e-06 4.10e-06 4.71e-05 1.61e-05
ShotgunWGS-TomatoPig14GutMicrobiome-Day7 14 Tomato Day 7 Tomato Day 7 0.0013116 1.09e-04 0.0008422 3.41e-05 0.0003321 0.0003254 0.0000749 0.0094690 5.8e-06 2.25e-05 8.32e-05 8.49e-05 0.0001123 0.0000940 0.0002788 0.0001623 0.0003678 0.0008322 0.0003370 7.57e-05 0.0001123 6.70e-06 0.0010511 8.3e-06 1.25e-05 0.0002189 0.0001914 1.08e-05 5.0e-06 0.0004810 0.0001298 0.0001523 0.0000866 2.91e-05 0.0004619 0.0003254 0.0011734 0.0001099 0.0044083 9.15e-05 2.00e-05 0.0001639 0.0002014 4.58e-05 0.0002314 4.33e-05 0.0001872 7.32e-05 0.0013116 0.0006342 0.0003803 0.0016178 0.0029386 1.50e-05 0.0004427 0.0001415 0.0003687 0.0001806 0.0002022 0.0001290 0.0003379 3.3e-06 3.66e-05 3.58e-05 5.16e-05 0.0029136 3.16e-05 0.0001323 5.58e-05 6.57e-05 8.99e-05 2.5e-06 0.0083622 0.0892998 6.57e-05 0.0002755 0.0001232 4.16e-05 3.08e-05 5.08e-05 0.0001689 0.0046122 0.0000874 3.33e-05 0.0059104 0.0002480 0.0001773 2.41e-05 3.30e-06 0.0001423 0.0015346 0.0001997 0.0004985 0.0001007 3.83e-05 0.0001332 4.49e-05 9.90e-05 0.0005776 0.0008663 0.0090321 5.49e-05 0.0021047 0.0029727 0.0001015 5.80e-06 1.75e-05 0.0017227 1.75e-05 0.0000699 6.99e-05 0.0002929 2.66e-05 3.00e-05 0.0004402 3.58e-05 3.00e-05 0.0001997 7.5e-06 2.66e-05 8.07e-05 4.91e-05 1.75e-05 2.83e-05 5.0e-06 0.0003537 9.20e-06 0.0007041 0.0012392 3.41e-05 8.41e-05 0.0011451 8.66e-05 6.99e-05 0.0001872 0.0001074 0.0009454 0.0001598 1.25e-05 5.0e-06 0.0000741 0.0006317 2.33e-05 3.00e-05 4.16e-05 8.0e-07 0.0002122 0.0006133 0.0002563 0.0001798 3.3e-06 0.0001481 0.0001440 0.0001140 2.75e-05 1.00e-05 0.0003412 4.20e-06 0.0001706 5.80e-06 0.0910882 7.50e-06 0.0026098 0.0001781 0.0000516 9.40e-05 4.58e-05 1.17e-05 0.0003920 0.0047045 0.0001182 0.0000682 0.0026390 8.41e-05 0.0001531 7.99e-05 0.0002230 0.0007665 1.58e-05 0.0002663 2.5e-06 4.2e-06 0.0000108 2.5e-06 0.0004827 1.91e-05 0.0004752 5.0e-06 0.0001606 0.0001357 0.0009454 0.0001298 0.0003088 0.0000674 0.0002147 2.00e-05 0.0001123 0.0002380 0.0042968 0.0001664 0.0002114 0.0001148 0.0001723 3.25e-05 0.0002530 0.0028811 0.0032182 9.49e-05 9.32e-05 1.08e-05 0.0001997 0.0001823 0.0003262 0.0011618 9.15e-05 0.0002405 0.0003529 7.82e-05 4.91e-05 0.0001806 0.0068667 0.0004660 0.0002139 0.0023435 3.16e-05 1.91e-05 0.0002056 2.33e-05 4.2e-06 5.0e-06 1.08e-05 3.50e-05 0.0000399 0.0003728 0.0036326 1.41e-05 2.5e-06 0.0002413 5.91e-05 1.41e-05 0.0001748 0.0001015 0.0000924 0.0100665 0.0060960 0.0458013 0.0005767 0.0398517 0.0001631 2.91e-05 3.66e-05 0.0002122 0.0014564 0.0002538 2.83e-05 0.0011035 0.0014522 0.0002372 0.0002746 9.20e-06 0.0024900 5.41e-05 0.0e+00 0.0004253 9.82e-05 2.58e-05 0.0000508 0.0020597 0.0016245 7.91e-05 5.80e-06 3.41e-05 0.0001465 5.49e-05 4.83e-05 5.74e-05 1.7e-06 0.0005176 6.66e-05 0.0001581 7.5e-06 0.0007066 0.0001806 2.00e-05 0.0002530 0.0001115 3.50e-05 2.16e-05 1.91e-05 2.33e-05 1.17e-05 5.66e-05 2.41e-05 2.66e-05 9.15e-05 1.83e-05 2.91e-05 0.0006084 6.49e-05 0.0029286 0.0014098 7.74e-05 0.0001015 0.0001664 2.75e-05 0.0002971 1.83e-05 0.0036768 3.74e-05 4.74e-05 6.24e-05 1.50e-05 2.08e-05 5.66e-05 0.0002505 2.41e-05 1.0e-05 0.0005934 4.33e-05 6.32e-05 4.91e-05 0.0001148 0.0001132 0.0001315 6.66e-05 2.41e-05 0.0001881 2.16e-05 0.0005077 1.58e-05 0.0001165 8.57e-05 0.0001523 8.82e-05 6.91e-05 7.49e-05 0.0e+00 4.99e-05 8.3e-06 6.7e-06 0.0870628 0.0006733 0.0001173 7.49e-05 0.0001997 0.0003620 0.0004244 0.0002155 0.0000866 3.08e-05 7.07e-05 8e-07 0.0001972 0.0e+00 0.0000882 0.0006200 0.0004536 3.41e-05 0.0013740 1.50e-05 5.0e-06 2.08e-05 3.00e-05 2.00e-05 0.0003878 0.0001714 2.41e-05 0.0001756 0.0001872 1.33e-05 0.0001556 0.0003370 5.58e-05 0.0002480 0.0002314 3.00e-05 3.00e-05 0.0001956 0.0051190 0.0001906 0.0001074 0.0000915 1.91e-05 0.0022853 0.0002272 7.24e-05 0.0001490 0.0004469 0.0004003 0.0001273 4.49e-05 6.74e-05 0.0001015 6.57e-05 9.99e-05 0.0001032 0.0006366 0.0001631 7.49e-05 0.0001381 0.0003004 1.91e-05 4.83e-05 5.33e-05 0.0000816 8.90e-05 3.66e-05 0.0001947 3.99e-05 0.0001515 2.33e-05 1.83e-05 6.91e-05 5.66e-05 8.3e-06 5.83e-05 2.25e-05 9.65e-05 2.33e-05 6.99e-05 0.0001273 0.0064530 0.0042194 4.2e-06 3.83e-05 0.0011343 4.83e-05 0.0001132 0.0001848 0.0005975 0.0004952 0.0001947 1.7e-06 2.25e-05 8.07e-05 3.25e-05 5.0e-06 0.0004985 1.08e-05 1.75e-05 5.91e-05 1.0e-05 0.0004253 1.58e-05 4.58e-05 3.74e-05 2.50e-05 0.0001090 0.0001165 4.08e-05 0.0002130 0.0001989 1.83e-05 9.65e-05 7.66e-05 0.0001173 0.0001823 5.58e-05 2.00e-05 0.0002139 0.0000666 1.0e-05 2.58e-05 3.16e-05 5.99e-05 0.0004977 0.0000782 2.16e-05 3.3e-06 0.0002380 3.16e-05 0.0019391 0.0002455 0.0028795 1.83e-05 3.66e-05 2.41e-05 4.49e-05 0.0003146 0.0001049 5.0e-06 4.2e-06 0.0024667 0.0015330 0.0002413 0.0087625 1.58e-05 8.0e-07 6.49e-05 7.50e-06 0.0000641 8.99e-05 4.24e-05 0.0002538 5.8e-06 0.0003071 0.0005118 0.0010345 8.30e-06 0.0007898 0.0002522 0.0010827 1.00e-05 0.0007490 0.0005110 1.41e-05 5.66e-05 0.0002538 1.08e-05 2.66e-05 1.33e-05 4.41e-05 8.0e-07 0.0005709 0.0002413 3.16e-05 6.7e-06 4.16e-05 4.49e-05 0.0001257 0.0001057 4.74e-05 5.0e-06 0.0002630 0.0001748 0.0000832 7.5e-06 0.0028529 8.0e-07 0.2312992 0.0002297 0.0004169 4.49e-05 0.0002430 0.0001323 0.0005426 0.0012242 0.0005243 1.58e-05 0.0002189 6.57e-05 0.0003961 0.0005368 1.08e-05 4.41e-05 0.0001806 0.0001997 5.8e-06 3.3e-06 9.40e-05 5.74e-05 0.0002355 0.0001573 0.0002480 3.74e-05 8.0e-07 0.0001656 0.0003645 0.0002372 0.0002056 9.40e-05 1.66e-05 0.0001456 0.0002646 0.0101514 3.30e-06 0.0004635 0.0001573 1.83e-05 4.49e-05 0.0001207 0.0002155 0.0001223 0.0483745 8.0e-07 1.25e-05 2.5e-06 6.32e-05 3.41e-05 0.0002255 0.0001165 4.2e-06 9.20e-06 0.0001390 0.0001307 0.0008505 0.0001648 0.0000674 1.66e-05 9.2e-06 3.91e-05 5.0e-06 0.0006849 1.50e-05 0.0092501 0.0002788 0.0015912 0.0012483 0.0009063 5.24e-05 4.49e-05 0.0001856 0.0028062 8.82e-05 0.0001015 0.0002239 0.0001748 5.16e-05 9.99e-05 6.32e-05 0.0009812 0.0006733 6.08e-05 0.0026731 1.83e-05 4.74e-05 0.0001257 3.83e-05 0.0002264 0.0075358 0.0006533 0.0001157 0.0210211 2.25e-05 6.99e-05 5.08e-05 0.0001581 0.0000999 9.32e-05 9.82e-05 0.0009737 0.0007082 0.0001365 0.0002788 0.0011318 0.0003412 0.0002929 1.50e-05 4.2e-06 7.50e-06 8.66e-05 6.66e-05 7.5e-06 1.25e-05 2.41e-05 5.0e-06 5.49e-05 9.2e-06 0.0002971 0.0002413 0.0008139 0.0019957 0.0008655 0.0001631 0.0001615 7.24e-05 0.0002014 3.50e-05 0.0001240 2.75e-05 9.24e-05 9.15e-05 8.41e-05 2.5e-06 0.0006816 0.0012350 0.0002971 4.2e-06 0.0001315 0.0006699 0.0001956 0.0001357 0.0000965 0.0001215 5.33e-05 0.0005576 6.70e-06 0.0017984 0.0001440 0.0002089 1.58e-05 2.16e-05 4.99e-05 1.58e-05 3.99e-05 5.0e-06 0.0003379 6.7e-06 6.66e-05 1.91e-05 9.20e-06 0.0000558 0.0039406 0.0000699 3.74e-05 5.8e-06 0.0014372 0.0003387 4.91e-05 7.5e-06 5.58e-05 6.08e-05 1.41e-05 6.16e-05 0.0002164 7.16e-05 0.0003021 0.0001049 0.0001723 0.0001007 2.50e-05 0.0006425 0.0004169 3.3e-06 5.24e-05 5.0e-06 5.33e-05 0.0003137 2.66e-05 6.41e-05 0.0007731 0.0003437 8.3e-06 0.0003146 1.58e-05 1.41e-05 4.91e-05 1.41e-05 0.0001731 0.0036817 7.74e-05 9.32e-05 0.0045048 0.0001615 0.0002364 0.0001689 0.0001939 0.0002971 0.0046013 1.08e-05 0.0000691 0.0000499 2.50e-06 3.41e-05 6.66e-05 4.2e-06 0.0092876 0.0003287 7.74e-05 0.0000458 3.91e-05 8.82e-05 2.58e-05 7.32e-05 9.57e-05
ShotgunWGS-ControlPig5GutMicrobiome-Day7 5 Control Day 7 Control Day 7 0.0012072 7.49e-05 0.0006482 2.08e-05 0.0002852 0.0002562 0.0000902 0.0096632 5.2e-06 2.93e-05 6.67e-05 7.91e-05 0.0001110 0.0000990 0.0003871 0.0001449 0.0002917 0.0005789 0.0002028 5.83e-05 0.0000729 1.53e-05 0.0005483 8.8e-06 1.01e-05 0.0001247 0.0001608 9.80e-06 6.2e-06 0.0003953 0.0001673 0.0001110 0.0000983 4.95e-05 0.0003809 0.0002106 0.0011405 0.0000879 0.0039948 6.67e-05 2.30e-06 0.0001087 0.0002058 3.42e-05 0.0002653 5.47e-05 0.0001459 4.53e-05 0.0010314 0.0004802 0.0003692 0.0012463 0.0028801 1.40e-05 0.0003718 0.0001426 0.0001703 0.0001426 0.0001498 0.0001354 0.0002393 1.6e-06 2.87e-05 2.83e-05 4.07e-05 0.0041645 3.22e-05 0.0001322 5.99e-05 7.16e-05 7.33e-05 2.0e-06 0.0072689 0.0733720 6.71e-05 0.0002191 0.0000957 2.18e-05 3.29e-05 2.08e-05 0.0000710 0.0215087 0.0000977 3.13e-05 0.0054932 0.0002702 0.0000853 7.80e-06 2.00e-06 0.0000661 0.0013678 0.0002178 0.0004516 0.0000697 3.42e-05 0.0000840 1.17e-05 6.58e-05 0.0003572 0.0008638 0.0094401 3.65e-05 0.0018766 0.0026456 0.0000794 9.80e-06 1.33e-05 0.0019229 1.17e-05 0.0001003 7.52e-05 0.0002631 1.86e-05 2.70e-05 0.0004040 1.86e-05 2.15e-05 0.0001853 8.5e-06 2.34e-05 5.70e-05 5.76e-05 1.73e-05 8.80e-06 1.6e-06 0.0003002 1.01e-05 0.0006124 0.0011304 2.51e-05 6.41e-05 0.0012369 6.28e-05 5.80e-05 0.0001778 0.0000622 0.0009132 0.0001084 7.80e-06 5.2e-06 0.0000794 0.0005675 2.80e-05 3.48e-05 3.68e-05 1.3e-06 0.0002136 0.0005825 0.0002299 0.0001426 2.9e-06 0.0001296 0.0001185 0.0000967 7.49e-05 1.01e-05 0.0001530 9.40e-06 0.0001009 2.60e-06 0.0886641 1.01e-05 0.0033316 0.0001162 0.0000632 9.67e-05 4.88e-05 6.20e-06 0.0003871 0.0061049 0.0000973 0.0000983 0.0005945 4.95e-05 0.0001410 4.88e-05 0.0001107 0.0008725 1.37e-05 0.0003116 2.0e-06 5.2e-06 0.0001953 1.3e-06 0.0004184 1.33e-05 0.0004356 4.6e-06 0.0002680 0.0001211 0.0008107 0.0000736 0.0002686 0.0001071 0.0001791 1.14e-05 0.0000908 0.0001980 0.0043077 0.0001374 0.0001680 0.0000850 0.0001449 1.82e-05 0.0002191 0.0027140 0.0024063 8.89e-05 6.77e-05 7.80e-06 0.0001556 0.0001696 0.0003060 0.0042192 7.94e-05 0.0001563 0.0002920 5.01e-05 3.97e-05 0.0001195 0.0107353 0.0004021 0.0001354 0.0025304 2.02e-05 1.56e-05 0.0001465 1.47e-05 1.0e-06 1.3e-06 1.20e-05 1.63e-05 0.0000716 0.0001644 0.0029729 2.47e-05 2.0e-06 0.0001755 4.23e-05 1.27e-05 0.0000967 0.0000726 0.0000964 0.0005408 0.0053714 0.0589401 0.0005489 0.0373568 0.0000879 2.54e-05 3.06e-05 0.0002038 0.0013153 0.0002188 1.60e-05 0.0007654 0.0010939 0.0001726 0.0002898 1.17e-05 0.0021713 5.93e-05 7.0e-07 0.0011454 5.63e-05 3.32e-05 0.0000687 0.0017737 0.0014238 5.34e-05 1.17e-05 2.47e-05 0.0001211 4.33e-05 4.98e-05 3.74e-05 0.0e+00 0.0004001 4.69e-05 0.0001400 4.9e-06 0.0004304 0.0001504 1.14e-05 0.0002432 0.0000902 2.80e-05 2.67e-05 1.37e-05 1.79e-05 1.27e-05 4.30e-05 1.66e-05 2.60e-05 6.87e-05 1.30e-05 2.25e-05 0.0005639 3.61e-05 0.0006163 0.0013590 7.81e-05 0.0000980 0.0001325 3.45e-05 0.0001957 1.14e-05 0.0035511 2.21e-05 3.26e-05 4.75e-05 1.33e-05 2.08e-05 6.12e-05 0.0001546 1.37e-05 7.8e-06 0.0006264 3.13e-05 4.40e-05 4.40e-05 0.0001130 0.0000733 0.0000791 6.22e-05 2.41e-05 0.0001022 1.99e-05 0.0002061 7.50e-06 0.0000423 5.01e-05 0.0001149 5.47e-05 4.62e-05 4.10e-05 7.0e-07 2.34e-05 6.2e-06 5.5e-06 0.1788730 0.0006108 0.0000264 6.12e-05 0.0001481 0.0003174 0.0003262 0.0001784 0.0000563 2.05e-05 5.67e-05 7e-07 0.0001579 0.0e+00 0.0000967 0.0006316 0.0003917 3.09e-05 0.0012903 4.20e-06 7.8e-06 1.66e-05 3.16e-05 2.18e-05 0.0003080 0.0001374 1.53e-05 0.0001374 0.0001944 5.90e-06 0.0000866 0.0002360 4.13e-05 0.0002015 0.0001843 2.44e-05 3.16e-05 0.0001589 0.0052210 0.0001566 0.0000560 0.0001074 1.63e-05 0.0006486 0.0001735 6.87e-05 0.0001107 0.0003865 0.0003588 0.0001175 3.09e-05 4.46e-05 0.0000970 3.84e-05 8.37e-05 0.0000957 0.0005125 0.0001104 4.66e-05 0.0001208 0.0001293 8.10e-06 2.64e-05 4.23e-05 0.0001205 9.83e-05 2.38e-05 0.0002002 4.59e-05 0.0001299 1.95e-05 3.42e-05 6.38e-05 4.79e-05 4.2e-06 3.91e-05 2.21e-05 8.33e-05 1.86e-05 7.03e-05 0.0001032 0.0165768 0.0003607 2.3e-06 1.82e-05 0.0011503 3.48e-05 0.0000544 0.0001628 0.0004845 0.0003305 0.0001605 2.0e-06 1.47e-05 5.18e-05 1.99e-05 1.3e-06 0.0004503 1.79e-05 2.44e-05 4.92e-05 5.9e-06 0.0002989 1.07e-05 3.09e-05 2.93e-05 1.60e-05 0.0000964 0.0001029 3.78e-05 0.0001638 0.0001514 1.01e-05 9.60e-05 6.28e-05 0.0000674 0.0001345 5.27e-05 2.02e-05 0.0001996 0.0000944 6.5e-06 2.47e-05 3.61e-05 4.66e-05 0.0004350 0.0000713 1.95e-05 1.3e-06 0.0002256 2.96e-05 0.0029943 0.0002793 0.0015901 1.11e-05 2.38e-05 2.87e-05 3.65e-05 0.0002859 0.0000007 1.0e-06 7.0e-07 0.0023002 0.0012714 0.0001608 0.0071679 9.80e-06 2.0e-06 7.36e-05 1.27e-05 0.0002260 7.42e-05 3.26e-05 0.0001774 9.1e-06 0.0002246 0.0003835 0.0008931 1.11e-05 0.0006974 0.0002618 0.0011070 9.10e-06 0.0004975 0.0004252 1.17e-05 5.60e-05 0.0001966 5.20e-06 1.53e-05 9.80e-06 3.35e-05 1.0e-06 0.0003484 0.0001599 2.60e-05 3.9e-06 3.32e-05 7.98e-05 0.0001416 0.0000283 3.09e-05 3.9e-06 0.0002618 0.0002728 0.0001182 6.2e-06 0.0025476 2.0e-06 0.1651427 0.0002930 0.0002146 4.82e-05 0.0001400 0.0000824 0.0003090 0.0009214 0.0005196 1.73e-05 0.0001494 4.92e-05 0.0002377 0.0005493 2.90e-06 3.22e-05 0.0001390 0.0002292 6.8e-06 7.0e-07 6.87e-05 3.71e-05 0.0002116 0.0002188 0.0002009 2.67e-05 1.0e-06 0.0001927 0.0003321 0.0001905 0.0001885 6.97e-05 9.40e-06 0.0001322 0.0002061 0.0153119 1.11e-05 0.0004421 0.0001289 2.83e-05 5.18e-05 0.0000697 0.0001914 0.0001286 0.0422350 1.3e-06 1.27e-05 7.0e-07 3.65e-05 2.31e-05 0.0001872 0.0001143 2.6e-06 1.11e-05 0.0001234 0.0001127 0.0002725 0.0001127 0.0001856 1.50e-05 8.5e-06 1.92e-05 4.9e-06 0.0006779 1.66e-05 0.0068267 0.0001872 0.0010484 0.0000680 0.0009028 5.80e-05 2.21e-05 0.0001973 0.0030057 5.67e-05 0.0001107 0.0001895 0.0001761 5.18e-05 9.90e-05 7.55e-05 0.0007739 0.0005623 5.31e-05 0.0013756 1.76e-05 3.58e-05 0.0000866 4.53e-05 0.0002100 0.0133708 0.0005942 0.0000876 0.0141685 1.86e-05 4.07e-05 3.81e-05 0.0001328 0.0000964 8.47e-05 9.31e-05 0.0009048 0.0013508 0.0001280 0.0002507 0.0009702 0.0003187 0.0002953 1.37e-05 2.3e-06 6.80e-06 5.57e-05 5.99e-05 2.0e-06 7.80e-06 1.92e-05 6.2e-06 8.30e-05 4.6e-06 0.0002735 0.0001993 0.0007003 0.0017239 0.0008599 0.0001302 0.0001061 6.80e-05 0.0001748 2.80e-05 0.0000944 2.67e-05 8.14e-05 6.02e-05 7.07e-05 3.6e-06 0.0005691 0.0008680 0.0002946 1.6e-06 0.0001087 0.0006219 0.0001566 0.0001133 0.0001055 0.0000912 3.61e-05 0.0003002 7.20e-06 0.0011174 0.0001091 0.0001520 8.80e-06 1.92e-05 5.37e-05 1.50e-05 3.39e-05 4.6e-06 0.0002653 4.2e-06 5.21e-05 1.20e-05 5.90e-06 0.0001048 0.0042954 0.0001094 5.93e-05 1.3e-06 0.0009344 0.0003793 3.22e-05 7.2e-06 3.22e-05 3.87e-05 7.50e-06 4.72e-05 0.0000964 8.24e-05 0.0002540 0.0000628 0.0001169 0.0000964 1.69e-05 0.0003506 0.0003139 5.5e-06 6.25e-05 3.9e-06 4.33e-05 0.0003220 4.04e-05 3.68e-05 0.0002735 0.0003340 9.8e-06 0.0001953 1.40e-05 7.20e-06 3.32e-05 8.50e-06 0.0001201 0.0034580 6.19e-05 7.00e-05 0.0043846 0.0001260 0.0001605 0.0001384 0.0001136 0.0001976 0.0048931 1.04e-05 0.0000563 0.0000619 2.00e-06 2.41e-05 3.09e-05 3.3e-06 0.0079575 0.0002338 4.20e-05 0.0000641 4.53e-05 3.97e-05 1.33e-05 6.51e-05 4.20e-06
ShotgunWGS-TomatoPig18GutMicrobiome-Day7 18 Tomato Day 7 Tomato Day 7 0.0006501 8.19e-05 0.0003281 1.85e-05 0.0001486 0.0001896 0.0001094 0.0059482 3.4e-06 4.77e-05 7.80e-05 7.57e-05 0.0000959 0.0001464 0.0006512 0.0000813 0.0002064 0.0003556 0.0002956 9.65e-05 0.0000561 2.75e-05 0.0001997 6.7e-06 7.30e-06 0.0000645 0.0001498 9.50e-06 5.0e-06 0.0004022 0.0002872 0.0000583 0.0001049 7.74e-05 0.0002737 0.0001032 0.0009177 0.0000780 0.0024922 5.05e-05 5.16e-05 0.0000415 0.0001099 1.91e-05 0.0001262 8.30e-05 0.0001285 2.97e-05 0.0007864 0.0002524 0.0003326 0.0008061 0.0016144 1.51e-05 0.0002620 0.0001027 0.0001369 0.0000830 0.0001088 0.0001027 0.0003999 1.1e-06 2.08e-05 4.43e-05 5.83e-05 0.0085061 2.64e-05 0.0001105 6.17e-05 5.89e-05 4.71e-05 2.8e-06 0.0049581 0.0944738 3.76e-05 0.0001200 0.0000819 1.46e-05 4.09e-05 1.12e-05 0.0001139 0.0585603 0.0000987 2.52e-05 0.0040758 0.0002507 0.0000752 9.50e-06 7.30e-06 0.0000931 0.0011628 0.0002592 0.0002816 0.0001212 5.72e-05 0.0000752 2.75e-05 1.80e-05 0.0002120 0.0007567 0.0061681 3.25e-05 0.0012464 0.0018191 0.0000611 7.90e-06 6.20e-06 0.0006967 5.60e-06 0.0000724 4.88e-05 0.0002345 1.07e-05 1.40e-05 0.0002406 4.50e-06 1.40e-05 0.0001975 3.9e-06 2.41e-05 2.69e-05 3.87e-05 1.51e-05 2.20e-06 0.0e+00 0.0004336 5.60e-06 0.0006372 0.0006221 1.12e-05 3.20e-05 0.0006283 9.98e-05 3.25e-05 0.0002098 0.0001127 0.0004213 0.0001251 6.20e-06 2.2e-06 0.0000954 0.0006995 2.19e-05 3.59e-05 2.86e-05 0.0e+00 0.0001868 0.0005901 0.0002087 0.0001402 9.0e-06 0.0000970 0.0000740 0.0001139 9.42e-05 7.30e-06 0.0002575 1.07e-05 0.0002227 8.40e-06 0.0567714 6.70e-06 0.0087798 0.0000836 0.0001027 8.41e-05 3.76e-05 6.70e-06 0.0002182 0.0031340 0.0000567 0.0000965 0.0010394 4.09e-05 0.0001144 3.93e-05 0.0001060 0.0009912 5.60e-06 0.0003332 6.0e-07 3.4e-06 0.0003949 2.8e-06 0.0003444 1.12e-05 0.0004426 6.2e-06 0.0001515 0.0000583 0.0005839 0.0001290 0.0002485 0.0001391 0.0001301 2.08e-05 0.0000595 0.0001172 0.0027587 0.0001094 0.0001335 0.0000611 0.0000976 1.85e-05 0.0001677 0.0016553 0.0010131 5.10e-05 6.45e-05 7.90e-06 0.0001217 0.0000578 0.0001761 0.0018118 5.67e-05 0.0001111 0.0002373 3.48e-05 4.49e-05 0.0001234 0.0048387 0.0004465 0.0000724 0.0024844 1.68e-05 1.12e-05 0.0000869 1.57e-05 0.0e+00 6.0e-07 3.40e-06 7.90e-06 0.0000174 0.0001975 0.0023369 1.68e-05 0.0e+00 0.0000954 2.30e-05 1.57e-05 0.0000898 0.0000359 0.0001200 0.0090541 0.0048308 0.0372300 0.0003798 0.0766393 0.0000337 1.96e-05 1.74e-05 0.0001374 0.0011617 0.0000948 2.24e-05 0.0004942 0.0016200 0.0001251 0.0004297 1.01e-05 0.0013552 3.70e-05 2.8e-06 0.0027166 2.47e-05 5.10e-05 0.0001167 0.0011976 0.0011370 8.98e-05 2.20e-06 3.93e-05 0.0001296 4.09e-05 5.95e-05 5.16e-05 6.0e-07 0.0003803 7.52e-05 0.0000836 3.4e-06 0.0002418 0.0000948 7.90e-06 0.0001257 0.0000808 3.03e-05 1.12e-05 7.90e-06 1.12e-05 1.18e-05 3.37e-05 1.46e-05 1.23e-05 8.02e-05 7.90e-06 2.19e-05 0.0003607 3.81e-05 0.0005200 0.0007629 9.70e-05 0.0000797 0.0001369 2.24e-05 0.0001027 8.40e-06 0.0027587 1.07e-05 1.46e-05 3.98e-05 7.90e-06 4.71e-05 5.55e-05 0.0000684 5.00e-06 3.4e-06 0.0003579 7.29e-05 9.37e-05 4.94e-05 0.0001105 0.0001335 0.0000578 3.03e-05 2.47e-05 0.0001829 5.00e-06 0.0003046 1.29e-05 0.0000639 4.82e-05 0.0000797 9.82e-05 3.48e-05 6.00e-05 1.1e-06 2.52e-05 2.8e-06 9.0e-06 0.1042975 0.0005228 0.0001430 4.15e-05 0.0001015 0.0003433 0.0004684 0.0001329 0.0001071 1.74e-05 2.86e-05 0e+00 0.0001318 1.1e-06 0.0001879 0.0004465 0.0003758 4.04e-05 0.0009199 1.51e-05 6.2e-06 2.36e-05 1.68e-05 1.63e-05 0.0002059 0.0000735 7.30e-06 0.0001127 0.0001509 5.60e-06 0.0000684 0.0002485 4.60e-05 0.0001257 0.0001167 1.29e-05 2.58e-05 0.0001357 0.0064643 0.0001402 0.0000370 0.0001481 1.12e-05 0.0003876 0.0000987 3.93e-05 0.0000892 0.0002395 0.0002721 0.0000959 2.19e-05 2.08e-05 0.0000645 3.14e-05 6.84e-05 0.0000533 0.0002850 0.0000959 3.87e-05 0.0000791 0.0000825 1.07e-05 1.01e-05 4.04e-05 0.0002311 7.24e-05 2.80e-05 0.0002474 7.24e-05 0.0001245 8.40e-06 7.01e-05 4.04e-05 3.98e-05 5.6e-06 4.99e-05 2.30e-05 8.13e-05 1.46e-05 8.47e-05 0.0000864 0.0049536 0.0005694 3.4e-06 1.35e-05 0.0006754 1.63e-05 0.0000191 0.0001784 0.0007500 0.0002221 0.0001834 0.0e+00 1.57e-05 8.86e-05 2.13e-05 6.0e-07 0.0002850 1.07e-05 7.90e-06 2.36e-05 5.0e-06 0.0001778 7.90e-06 3.20e-05 1.07e-05 9.50e-06 0.0000628 0.0001172 3.31e-05 0.0001285 0.0001464 8.40e-06 6.28e-05 4.54e-05 0.0000993 0.0002754 6.84e-05 1.29e-05 0.0001946 0.0001335 7.3e-06 3.03e-05 4.66e-05 4.43e-05 0.0002765 0.0000774 1.23e-05 0.0e+00 0.0001930 3.48e-05 0.0061911 0.0004028 0.0009514 7.30e-06 2.19e-05 2.02e-05 2.92e-05 0.0000864 0.0000673 1.1e-06 8.4e-06 0.0015790 0.0017703 0.0000987 0.0087316 8.40e-06 6.0e-07 6.23e-05 1.29e-05 0.0004970 8.02e-05 3.03e-05 0.0000639 9.5e-06 0.0001380 0.0004347 0.0009979 1.68e-05 0.0005082 0.0002822 0.0006815 7.30e-06 0.0002535 0.0002137 1.40e-05 4.21e-05 0.0001397 6.70e-06 2.02e-05 9.50e-06 3.98e-05 1.1e-06 0.0001616 0.0000869 1.40e-05 3.9e-06 2.19e-05 9.42e-05 0.0001700 0.0000409 3.93e-05 6.2e-06 0.0002889 0.0005312 0.0001862 3.4e-06 0.0026594 2.2e-06 0.2682445 0.0004056 0.0003629 3.37e-05 0.0000746 0.0000337 0.0001874 0.0008173 0.0003281 8.40e-06 0.0000993 5.22e-05 0.0000993 0.0003136 3.90e-06 2.36e-05 0.0001004 0.0002132 6.2e-06 1.1e-06 4.09e-05 6.23e-05 0.0002042 0.0002563 0.0002760 3.20e-05 0.0e+00 0.0002008 0.0003012 0.0001526 0.0001767 7.18e-05 2.20e-06 0.0001027 0.0002047 0.0088533 9.50e-06 0.0004117 0.0001498 3.76e-05 5.16e-05 0.0000864 0.0002025 0.0001212 0.0264808 6.0e-07 5.00e-06 3.9e-06 4.60e-05 6.51e-05 0.0002350 0.0002031 1.1e-06 1.07e-05 0.0001105 0.0001851 0.0006002 0.0001700 0.0003214 1.35e-05 2.2e-06 2.24e-05 4.5e-06 0.0004824 1.29e-05 0.0013463 0.0001386 0.0006204 0.0011297 0.0005278 4.71e-05 3.59e-05 0.0001986 0.0028008 2.58e-05 0.0001077 0.0002216 0.0001481 7.12e-05 1.29e-04 9.48e-05 0.0004717 0.0008341 5.67e-05 0.0016879 1.29e-05 4.43e-05 0.0000813 6.34e-05 0.0002008 0.0057126 0.0008779 0.0001212 0.0215249 2.52e-05 2.97e-05 2.41e-05 0.0001419 0.0000561 3.93e-05 4.21e-05 0.0006226 0.0019566 0.0001156 0.0001705 0.0007130 0.0001666 0.0001784 2.08e-05 2.8e-06 2.20e-06 4.94e-05 4.54e-05 2.2e-06 5.60e-06 1.57e-05 3.4e-06 6.45e-05 1.7e-06 0.0001924 0.0001251 0.0003489 0.0011656 0.0005121 0.0001111 0.0001694 8.98e-05 0.0000959 1.74e-05 0.0000909 2.30e-05 7.91e-05 8.47e-05 4.94e-05 2.2e-06 0.0003237 0.0003781 0.0001750 5.6e-06 0.0001060 0.0004005 0.0001228 0.0000909 0.0000847 0.0000471 4.99e-05 0.0000729 5.60e-06 0.0008106 0.0000875 0.0001043 1.63e-05 2.13e-05 6.23e-05 1.35e-05 4.66e-05 5.6e-06 0.0001178 1.7e-06 3.76e-05 2.13e-05 1.46e-05 0.0001733 0.0028142 0.0001761 8.47e-05 1.7e-06 0.0005396 0.0001329 4.04e-05 3.4e-06 2.69e-05 3.81e-05 6.20e-06 3.59e-05 0.0000701 8.64e-05 0.0002721 0.0000376 0.0001840 0.0000595 1.01e-05 0.0003068 0.0004129 6.7e-06 6.06e-05 2.8e-06 7.80e-05 0.0001761 2.97e-05 5.60e-06 0.0006776 0.0002193 2.8e-06 0.0000668 8.40e-06 9.50e-06 2.30e-05 6.70e-06 0.0000707 0.0024249 4.71e-05 4.21e-05 0.0022275 0.0000791 0.0001526 0.0001638 0.0000920 0.0001531 0.0029915 3.40e-06 0.0000589 0.0000561 1.07e-05 1.80e-05 1.40e-05 4.5e-06 0.0069882 0.0002317 2.69e-05 0.0001021 4.54e-05 6.70e-06 5.00e-06 4.88e-05 3.31e-05
# move Sample_Name to rownames, remove metadata
RelAbund.Genus.Filt.zerofilt.alphadiv <- RelAbund.Genus.Filt.zerofilt

rownames(RelAbund.Genus.Filt.zerofilt.alphadiv) <- RelAbund.Genus.Filt.zerofilt.alphadiv$Sample_Name  

RelAbund.Genus.Filt.zerofilt.alphadiv[1:5,1:8]
##                                                                         Sample_Name
## ShotgunWGS-ControlPig6GutMicrobiome-Day14 ShotgunWGS-ControlPig6GutMicrobiome-Day14
## ShotgunWGS-ControlPig8GutMicrobiome-Day0   ShotgunWGS-ControlPig8GutMicrobiome-Day0
## ShotgunWGS-ControlPig3GutMicrobiome-Day14 ShotgunWGS-ControlPig3GutMicrobiome-Day14
## ShotgunWGS-TomatoPig14GutMicrobiome-Day7   ShotgunWGS-TomatoPig14GutMicrobiome-Day7
## ShotgunWGS-ControlPig5GutMicrobiome-Day7   ShotgunWGS-ControlPig5GutMicrobiome-Day7
##                                           Pig    Diet Time_Point
## ShotgunWGS-ControlPig6GutMicrobiome-Day14   6 Control     Day 14
## ShotgunWGS-ControlPig8GutMicrobiome-Day0    8 Control      Day 0
## ShotgunWGS-ControlPig3GutMicrobiome-Day14   3 Control     Day 14
## ShotgunWGS-TomatoPig14GutMicrobiome-Day7   14  Tomato      Day 7
## ShotgunWGS-ControlPig5GutMicrobiome-Day7    5 Control      Day 7
##                                           Diet_By_Time_Point Abiotrophia
## ShotgunWGS-ControlPig6GutMicrobiome-Day14     Control Day 14 0.001305713
## ShotgunWGS-ControlPig8GutMicrobiome-Day0       Control Day 0 0.001347804
## ShotgunWGS-ControlPig3GutMicrobiome-Day14     Control Day 14 0.001066255
## ShotgunWGS-TomatoPig14GutMicrobiome-Day7        Tomato Day 7 0.001311580
## ShotgunWGS-ControlPig5GutMicrobiome-Day7       Control Day 7 0.001207244
##                                           Acaryochloris  Acetivibrio
## ShotgunWGS-ControlPig6GutMicrobiome-Day14  6.983388e-05 0.0005122869
## ShotgunWGS-ControlPig8GutMicrobiome-Day0   9.904370e-05 0.0007097339
## ShotgunWGS-ControlPig3GutMicrobiome-Day14  6.914992e-05 0.0005363651
## ShotgunWGS-TomatoPig14GutMicrobiome-Day7   1.090209e-04 0.0008422076
## ShotgunWGS-ControlPig5GutMicrobiome-Day7   7.488298e-05 0.0006482261
# remove metadata
RelAbund.Genus.Filt.zerofilt.alphadiv <- RelAbund.Genus.Filt.zerofilt.alphadiv %>%
  select(Abiotrophia:ncol(.))

RelAbund.Genus.Filt.zerofilt.alphadiv[1:5,1:5]
##                                           Abiotrophia Acaryochloris
## ShotgunWGS-ControlPig6GutMicrobiome-Day14 0.001305713  6.983388e-05
## ShotgunWGS-ControlPig8GutMicrobiome-Day0  0.001347804  9.904370e-05
## ShotgunWGS-ControlPig3GutMicrobiome-Day14 0.001066255  6.914992e-05
## ShotgunWGS-TomatoPig14GutMicrobiome-Day7  0.001311580  1.090209e-04
## ShotgunWGS-ControlPig5GutMicrobiome-Day7  0.001207244  7.488298e-05
##                                            Acetivibrio  Acetobacter
## ShotgunWGS-ControlPig6GutMicrobiome-Day14 0.0005122869 1.700751e-05
## ShotgunWGS-ControlPig8GutMicrobiome-Day0  0.0007097339 2.047538e-05
## ShotgunWGS-ControlPig3GutMicrobiome-Day14 0.0005363651 1.553931e-05
## ShotgunWGS-TomatoPig14GutMicrobiome-Day7  0.0008422076 3.412106e-05
## ShotgunWGS-ControlPig5GutMicrobiome-Day7  0.0006482261 2.083700e-05
##                                           Acetohalobium
## ShotgunWGS-ControlPig6GutMicrobiome-Day14  0.0002669664
## ShotgunWGS-ControlPig8GutMicrobiome-Day0   0.0003268918
## ShotgunWGS-ControlPig3GutMicrobiome-Day14  0.0002628733
## ShotgunWGS-TomatoPig14GutMicrobiome-Day7   0.0003320562
## ShotgunWGS-ControlPig5GutMicrobiome-Day7   0.0002852065
rownames(RelAbund.Genus.Filt.zerofilt.alphadiv)
##  [1] "ShotgunWGS-ControlPig6GutMicrobiome-Day14" 
##  [2] "ShotgunWGS-ControlPig8GutMicrobiome-Day0"  
##  [3] "ShotgunWGS-ControlPig3GutMicrobiome-Day14" 
##  [4] "ShotgunWGS-TomatoPig14GutMicrobiome-Day7"  
##  [5] "ShotgunWGS-ControlPig5GutMicrobiome-Day7"  
##  [6] "ShotgunWGS-TomatoPig18GutMicrobiome-Day7"  
##  [7] "ShotgunWGS-TomatoPig16GutMicrobiome-Day7"  
##  [8] "ShotgunWGS-ControlPig10GutMicrobiome-Day7" 
##  [9] "ShotgunWGS-ControlPig2GutMicrobiome-Day0"  
## [10] "ShotgunWGS-TomatoPig18GutMicrobiome-Day0"  
## [11] "ShotgunWGS-ControlPig10GutMicrobiome-Day0" 
## [12] "ShotgunWGS-ControlPig7GutMicrobiome-Day0"  
## [13] "ShotgunWGS-ControlPig8GutMicrobiome-Day14" 
## [14] "ShotgunWGS-TomatoPig11GutMicrobiome-Day0"  
## [15] "ShotgunWGS-TomatoPig19GutMicrobiome-Day0"  
## [16] "ShotgunWGS-TomatoPig17GutMicrobiome-Day14" 
## [17] "ShotgunWGS-ControlPig9GutMicrobiome-Day14" 
## [18] "ShotgunWGS-ControlPig10GutMicrobiome-Day14"
## [19] "ShotgunWGS-TomatoPig19GutMicrobiome-Day7"  
## [20] "ShotgunWGS-ControlPig5GutMicrobiome-Day14" 
## [21] "ShotgunWGS-ControlPig2GutMicrobiome-Day7"  
## [22] "ShotgunWGS-ControlPig6GutMicrobiome-Day7"  
## [23] "ShotgunWGS-TomatoPig12GutMicrobiome-Day0"  
## [24] "ShotgunWGS-TomatoPig14GutMicrobiome-Day0"  
## [25] "ShotgunWGS-ControlPig7GutMicrobiome-Day14" 
## [26] "ShotgunWGS-TomatoPig11GutMicrobiome-Day14" 
## [27] "ShotgunWGS-TomatoPig20GutMicrobiome-Day0"  
## [28] "ShotgunWGS-ControlPig9GutMicrobiome-Day0"  
## [29] "ShotgunWGS-TomatoPig11GutMicrobiome-Day7"  
## [30] "ShotgunWGS-TomatoPig13GutMicrobiome-Day7"  
## [31] "ShotgunWGS-TomatoPig17GutMicrobiome-Day0"  
## [32] "ShotgunWGS-TomatoPig19GutMicrobiome-Day14" 
## [33] "ShotgunWGS-TomatoPig13GutMicrobiome-Day0"  
## [34] "ShotgunWGS-ControlPig2GutMicrobiome-Day14" 
## [35] "ShotgunWGS-ControlPig1GutMicrobiome-Day7"  
## [36] "ShotgunWGS-TomatoPig15GutMicrobiome-Day7"  
## [37] "ShotgunWGS-TomatoPig15GutMicrobiome-Day0"  
## [38] "ShotgunWGS-TomatoPig12GutMicrobiome-Day7"  
## [39] "ShotgunWGS-TomatoPig14GutMicrobiome-Day14" 
## [40] "ShotgunWGS-TomatoPig20GutMicrobiome-Day14" 
## [41] "ShotgunWGS-ControlPig1GutMicrobiome-Day0"  
## [42] "ShotgunWGS-ControlPig4GutMicrobiome-Day14" 
## [43] "ShotgunWGS-ControlPig6GutMicrobiome-Day0"  
## [44] "ShotgunWGS-TomatoPig16GutMicrobiome-Day0"  
## [45] "ShotgunWGS-TomatoPig16GutMicrobiome-Day14" 
## [46] "ShotgunWGS-TomatoPig18GutMicrobiome-Day14" 
## [47] "ShotgunWGS-ControlPig7GutMicrobiome-Day7"  
## [48] "ShotgunWGS-ControlPig4GutMicrobiome-Day7"  
## [49] "ShotgunWGS-TomatoPig13GutMicrobiome-Day14" 
## [50] "ShotgunWGS-ControlPig8GutMicrobiome-Day7"  
## [51] "ShotgunWGS-TomatoPig15GutMicrobiome-Day14" 
## [52] "ShotgunWGS-TomatoPig12GutMicrobiome-Day14" 
## [53] "ShotgunWGS-TomatoPig20GutMicrobiome-Day7"  
## [54] "ShotgunWGS-ControlPig1GutMicrobiome-Day14" 
## [55] "ShotgunWGS-ControlPig3GutMicrobiome-Day0"  
## [56] "ShotgunWGS-ControlPig5GutMicrobiome-Day0"  
## [57] "ShotgunWGS-ControlPig4GutMicrobiome-Day0"  
## [58] "ShotgunWGS-ControlPig9GutMicrobiome-Day7"  
## [59] "ShotgunWGS-ControlPig3GutMicrobiome-Day7"  
## [60] "ShotgunWGS-TomatoPig17GutMicrobime-Day7"

Calculate alpha diversity

# run alpha diversity on phyla
genera.filt.div <- diversity(RelAbund.Genus.Filt.zerofilt.alphadiv, index = "shannon")

# convert to df
genera.filt.div.df <- as.data.frame(genera.filt.div)

# make column name 'shannon.phyla.filt'
colnames(genera.filt.div.df) <- "shannon.genera.filt"

head(genera.filt.div.df)
##                                           shannon.genera.filt
## ShotgunWGS-ControlPig6GutMicrobiome-Day14            3.454094
## ShotgunWGS-ControlPig8GutMicrobiome-Day0             3.724200
## ShotgunWGS-ControlPig3GutMicrobiome-Day14            3.405044
## ShotgunWGS-TomatoPig14GutMicrobiome-Day7             3.639925
## ShotgunWGS-ControlPig5GutMicrobiome-Day7             3.526991
## ShotgunWGS-TomatoPig18GutMicrobiome-Day7             3.281356

Combine shannon alpha diversity results with metadata

# compile genera metadata
genera.metadata <- RelAbund.Genus.Filt.zerofilt[,1:5]

# combine with metadata
genera.filt.div.df.meta <- cbind(genera.metadata, genera.filt.div.df)

head(genera.filt.div.df.meta)
##                                 Sample_Name Pig    Diet Time_Point
## 1 ShotgunWGS-ControlPig6GutMicrobiome-Day14   6 Control     Day 14
## 2  ShotgunWGS-ControlPig8GutMicrobiome-Day0   8 Control      Day 0
## 3 ShotgunWGS-ControlPig3GutMicrobiome-Day14   3 Control     Day 14
## 4  ShotgunWGS-TomatoPig14GutMicrobiome-Day7  14  Tomato      Day 7
## 5  ShotgunWGS-ControlPig5GutMicrobiome-Day7   5 Control      Day 7
## 6  ShotgunWGS-TomatoPig18GutMicrobiome-Day7  18  Tomato      Day 7
##   Diet_By_Time_Point shannon.genera.filt
## 1     Control Day 14            3.454094
## 2      Control Day 0            3.724200
## 3     Control Day 14            3.405044
## 4       Tomato Day 7            3.639925
## 5      Control Day 7            3.526991
## 6       Tomato Day 7            3.281356

Plotting

X axis by diet

alpha.diversity.genera.bydiet <- genera.filt.div.df.meta %>%
  ggplot(aes(x = Diet, y = shannon.genera.filt, fill = Diet_By_Time_Point)) +
  geom_boxplot(outlier.shape = NA) +
  geom_point(aes(fill = Diet_By_Time_Point), color = "black", alpha = 0.7, position=position_jitterdodge()) +
  scale_fill_manual(values = c("skyblue1", "dodgerblue", "royalblue4", 
                               "sienna1","firebrick3","tomato4")) +
  scale_color_manual(values = c("skyblue1", "dodgerblue", "royalblue4", 
                               "sienna1","firebrick3","tomato4")) +
  theme_minimal() +
  theme(axis.text.x = element_text(size = 12, color = "black")) +
  labs(x=NULL, 
       y="Shannon diversity index", 
       title = "Alpha Diversity",
       subtitle = "Shannon Index, Genera Level", 
       fill="Diet & Time Point")

alpha.diversity.genera.bydiet

ggsave("Figures/AlphaDiversityGenera_ByDiet_Boxplot.png", 
       plot = alpha.diversity.genera.bydiet, 
       dpi = 800, 
       width = 10, 
       height = 6)

X-axis by Day

genera.filt.div.df.meta <- genera.filt.div.df.meta %>%
  mutate(Time_Point = fct_relevel(Time_Point, c("Day 0", "Day 7", "Day 14")))

alpha.diversity.genera.bytime <- genera.filt.div.df.meta %>%
  ggplot(aes(x = Time_Point, y = shannon.genera.filt, fill = Diet_By_Time_Point)) +
  geom_boxplot(outlier.shape = NA) +
  geom_point(aes(fill = Diet_By_Time_Point), color = "black", alpha = 0.7, position=position_jitterdodge()) +
  scale_fill_manual(values = c("skyblue1", "dodgerblue", "royalblue4", 
                               "sienna1","firebrick3","tomato4")) +
  scale_color_manual(values = c("skyblue1", "dodgerblue", "royalblue4", 
                               "sienna1","firebrick3","tomato4")) +
  theme_minimal() +
  theme(axis.text.x = element_text(size = 12, color = "black")) +
  labs(x=NULL, 
       y="Shannon diversity index", 
       title = "Alpha Diversity",
       subtitle = "Shannon Index, Genera Level", 
       fill="Diet & Time Point")

alpha.diversity.genera.bytime

ggsave("Figures/AlphaDiversityGenera_ByTime_Boxplot.png", 
       plot = alpha.diversity.genera.bytime, 
       dpi = 800, 
       width = 7, 
       height = 5)

Statistics

Repeated measures ANOVA on Shannon alpha diversity

# must remove columns that aren't used in anova
head(genera.filt.div.df.meta) 
##                                 Sample_Name Pig    Diet Time_Point
## 1 ShotgunWGS-ControlPig6GutMicrobiome-Day14   6 Control     Day 14
## 2  ShotgunWGS-ControlPig8GutMicrobiome-Day0   8 Control      Day 0
## 3 ShotgunWGS-ControlPig3GutMicrobiome-Day14   3 Control     Day 14
## 4  ShotgunWGS-TomatoPig14GutMicrobiome-Day7  14  Tomato      Day 7
## 5  ShotgunWGS-ControlPig5GutMicrobiome-Day7   5 Control      Day 7
## 6  ShotgunWGS-TomatoPig18GutMicrobiome-Day7  18  Tomato      Day 7
##   Diet_By_Time_Point shannon.genera.filt
## 1     Control Day 14            3.454094
## 2      Control Day 0            3.724200
## 3     Control Day 14            3.405044
## 4       Tomato Day 7            3.639925
## 5      Control Day 7            3.526991
## 6       Tomato Day 7            3.281356
genera.filt.div.df.meta.foranova <- genera.filt.div.df.meta[,-c(1,5)]

head(genera.filt.div.df.meta.foranova)
##   Pig    Diet Time_Point shannon.genera.filt
## 1   6 Control     Day 14            3.454094
## 2   8 Control      Day 0            3.724200
## 3   3 Control     Day 14            3.405044
## 4  14  Tomato      Day 7            3.639925
## 5   5 Control      Day 7            3.526991
## 6  18  Tomato      Day 7            3.281356
genera.filt.alphadiv.anova <- 
  anova_test(data = genera.filt.div.df.meta.foranova,
             formula = shannon.genera.filt ~ Diet*Time_Point + Error(Pig/Time_Point),
             dv = shannon.genera.filt, 
             wid = Pig, 
             within = Time_Point, 
             between = Diet)

get_anova_table(genera.filt.alphadiv.anova)
## ANOVA Table (type II tests)
## 
##            Effect DFn DFd     F     p p<.05   ges
## 1            Diet   1  18 2.888 0.106       0.066
## 2      Time_Point   2  36 0.254 0.777       0.008
## 3 Diet:Time_Point   2  36 0.037 0.964       0.001
  • Non-significant effect of diet (p = 0.106)
  • Non-significant effect of timepoint (0.777)
  • Non-significant interaction of diet:time point (p = 0.964).

Check for normality

shapiro.test(genera.filt.div.df.meta.foranova$shannon.genera.filt)
## 
##  Shapiro-Wilk normality test
## 
## data:  genera.filt.div.df.meta.foranova$shannon.genera.filt
## W = 0.97004, p-value = 0.1466

Normal.

No need for posthoc test since no model parameters are significant.

ALDEx2

Quick introduction to anatomy of the aldex function

The aldex function does every step - data transformation and statistics
variable.name <- aldex(reads.data, variables.vector, mc.samples=#, test=“t”/“kw”, effect=T/F)
reads.data - your reads/count data, unchanged
variables.vector - a vector of the variables corresponding to sample groups, in SAME order as sample names (and therefore columns)
mc.samples - here you tell the function how many Monte Carlo sampels to use with an integer (128 is typical)
test - which test do you want, t-test and wilcoxon, or anova-like and kruskal wallace? (will always do the parametric and non-parametric) t = t-test and wilcoxon kw = anova-like and kruskal wallace
effect - do you want to incude effect results in output?

Key to aldex outputs - taken directly from vignette

  • we.ep - Expected P value of Welch’s t test
  • we.eBH - Expected Benjamini-Hochberg corrected P value of Welch’s t test
  • wi.ep - Expected P value of Wilcoxon rank test
  • wi.eBH - Expected Benjamini-Hochberg corrected P value of Wilcoxon test
  • kw.ep - Expected P value of Kruskal-Wallace test
  • kw.eBH - Expected Benjamini-Hochberg corrected P value of Kruskal-Wallace test
  • glm.ep - Expected P value of glm test
  • glm.eBH - Expected Benjamini-Hochberg corrected P value of glm test
  • rab.all - median clr value for all samples in the feature
  • rab.win.NS - median clr value for the NS group of samples
  • rab.win.S - median clr value for the S group of samples
  • dif.btw - median difference in clr values between S and NS groups
  • dif.win - median of the largest difference in clr values within S and NS groups
  • effect - median effect size: diff.btw / max(diff.win) for all instances
  • overlap - proportion of effect size that overlaps 0 (i.e. no effect)

ALDEx2 takes counts, not relative abundance.

We are using Benjamini Hochberg corrected pvalues, or we.eBH for t-tests (i.e., subsetting by time), and Benjamini-Hochberg corrected pvalues of the glm test glm.eBH for ANOVA tests (i.e., subsetting by diet)

Downloading ALDEx2

if (!requireNamespace("BiocManager", quietly = TRUE))
  install.packages("BiocManager")

BiocManager::install("ALDEx2")

Wrangling

Since we use counts for ALDEx2, we need to filter our counts data to include only the genera we ended up using in our final analysis

# this is the data set filtered to remove inplausible phyla, but still includes genera with a lot of missing values 
Genus.Counts.Filt[1:10,1:10]
## # A tibble: 10 × 10
##    domain    phylum   class order family genus `ShotgunWGS-Co…` `ShotgunWGS-Co…`
##    <chr>     <chr>    <chr> <chr> <chr>  <chr>            <dbl>            <dbl>
##  1 Viruses   unclass… uncl… Caud… Podov… AHJD…               29                0
##  2 Bacteria  Firmicu… Baci… Lact… Aeroc… Abio…             5067             5661
##  3 Eukaryota unclass… uncl… uncl… uncla… Acan…                0                0
##  4 Bacteria  Cyanoba… uncl… uncl… uncla… Acar…              271              416
##  5 Bacteria  Firmicu… Clos… Clos… Rumin… Acet…             1988             2981
##  6 Bacteria  Proteob… Alph… Rhod… Aceto… Acet…               66               86
##  7 Bacteria  Firmicu… Clos… Hala… Halob… Acet…             1036             1373
##  8 Bacteria  Teneric… Moll… Acho… Achol… Acho…              779             1269
##  9 Bacteria  Proteob… Beta… Burk… Alcal… Achr…              192              298
## 10 Bacteria  Firmicu… Nega… Sele… Acida… Acid…            50181            39909
## # … with 2 more variables: `ShotgunWGS-ControlPig3GutMicrobiome-Day14` <dbl>,
## #   `ShotgunWGS-TomatoPig14GutMicrobiome-Day7` <dbl>
dim(Genus.Counts.Filt)
## [1] 895  66
# final genera list (after filtering for zeros)
final_genera[1:10,]
##  [1] "Abiotrophia"     "Acaryochloris"   "Acetivibrio"     "Acetobacter"    
##  [5] "Acetohalobium"   "Acholeplasma"    "Achromobacter"   "Acidaminococcus"
##  [9] "Acidilobus"      "Acidimicrobium"
# how many final genera do we have?
dim(final_genera)
## [1] 755   1
# join to create a df with genera in rows, samples in columns
# filtered for genera used in this analysis
genera_counts_foraldex <- inner_join(final_genera, Genus.Counts.Filt,
                                     by = "genus")

dim(genera_counts_foraldex)
## [1] 755  66
# remove non-necessary metadata
genera_counts_foraldex <- genera_counts_foraldex[,-c(2:6)]

genera_counts_foraldex[1:10, 1:4]
##              genus ShotgunWGS-ControlPig6GutMicrobiome-Day14
## 1      Abiotrophia                                      5067
## 2    Acaryochloris                                       271
## 3      Acetivibrio                                      1988
## 4      Acetobacter                                        66
## 5    Acetohalobium                                      1036
## 6     Acholeplasma                                       779
## 7    Achromobacter                                       192
## 8  Acidaminococcus                                     50181
## 9       Acidilobus                                        10
## 10  Acidimicrobium                                        59
##    ShotgunWGS-ControlPig8GutMicrobiome-Day0
## 1                                      5661
## 2                                       416
## 3                                      2981
## 4                                        86
## 5                                      1373
## 6                                      1269
## 7                                       298
## 8                                     39909
## 9                                        20
## 10                                      126
##    ShotgunWGS-ControlPig3GutMicrobiome-Day14
## 1                                       4117
## 2                                        267
## 3                                       2071
## 4                                         60
## 5                                       1015
## 6                                        817
## 7                                        197
## 8                                      31994
## 9                                         25
## 10                                        81
# add genera as rownames
rownames(genera_counts_foraldex) <- genera_counts_foraldex$genus

# remove genera as column for cleaner data
genera_counts_foraldex <- genera_counts_foraldex %>%
  select(-genus)

Subset by Time

Day 0

Look at the effect of diet at day 0.

# subset day 0 only
Day0.Counts.Genera.filt <- genera_counts_foraldex %>% 
  select(ends_with("Day0"))

ALDEx2 function needs a factor of variables

# order alphabetically so making the meta data vector is easier
Day0.Counts.Genera.filt <- Day0.Counts.Genera.filt[order(colnames(Day0.Counts.Genera.filt))]

Diets.Day0.Genera <- as.vector(c(rep("Control", times=10), rep("Tomato", times=10)))

# check and make sure it came out right
Diets.Day0.Genera
##  [1] "Control" "Control" "Control" "Control" "Control" "Control" "Control"
##  [8] "Control" "Control" "Control" "Tomato"  "Tomato"  "Tomato"  "Tomato" 
## [15] "Tomato"  "Tomato"  "Tomato"  "Tomato"  "Tomato"  "Tomato"

Run t-test

filt.Genera.Day0.ByDiet.aldex <- aldex(Day0.Counts.Genera.filt, 
                                       Diets.Day0.Genera, 
                                       mc.samples = 1000, 
                                       test = "t", 
                                       effect = TRUE)
## aldex.clr: generating Monte-Carlo instances and clr values
## operating in serial mode
## computing center with all features
## aldex.ttest: doing t-test
## aldex.effect: calculating effect sizes
filt.Genera.Day0.ByDiet.aldex <- 
  filt.Genera.Day0.ByDiet.aldex[order(filt.Genera.Day0.ByDiet.aldex$we.eBH, 
                                      decreasing = FALSE),]

kable(head(filt.Genera.Day0.ByDiet.aldex))
rab.all rab.win.Control rab.win.Tomato diff.btw diff.win effect overlap we.ep we.eBH wi.ep wi.eBH
Methanoplanus 0.3201847 0.4587166 0.1585091 -0.2744457 0.2929463 -0.8900506 0.1761648 0.0161507 0.6571223 0.0206049 0.6391049
Herbaspirillum -0.5564932 -0.7855297 -0.3447862 0.4894062 0.5715672 0.8331084 0.1700000 0.0108595 0.6613759 0.0156222 0.6367983
Caenorhabditis -0.8681507 -0.7231369 -1.0313465 -0.3344422 0.4358249 -0.7236616 0.1917616 0.0233795 0.6654581 0.0326584 0.6445105
Gallionella -0.8191944 -0.9806255 -0.6565452 0.3378006 0.4275946 0.7312681 0.1807638 0.0233197 0.6656534 0.0259145 0.6331800
Epsilon15-like viruses -5.4155796 -6.3291178 -4.7770370 1.5282103 1.9392933 0.7162934 0.1777644 0.0342860 0.6681890 0.0276429 0.6289355
Collinsella 6.4097209 5.9122154 6.8773284 0.9055678 0.8808264 0.9277446 0.1979604 0.0098821 0.6683595 0.0208505 0.6554576

Create a histogram of pvalues of we.eBH

hist(filt.Genera.Day0.ByDiet.aldex$we.eBH,
     breaks = 20,
     main = "Histogram of p-values on the effect of diet at day 0 on genera",
     xlab = "Benjamini Hochberg corrected p-value (we.eBH)")

we.eBH is the Benjamini-Hochberg corrected p-value, no significantly different genera at day 0.

Day 7

Look at the effect of diet at day 7.

# subset day 7 only
Day7.Counts.Genera.filt <- genera_counts_foraldex %>% 
  select(ends_with("Day7"))

ALDEx2 function needs a factor of variables

# order alphabetically so making the meta data vector is easier
Day7.Counts.Genera.filt <- Day7.Counts.Genera.filt[order(colnames(Day7.Counts.Genera.filt))]

Diets.Day7.Genera <- as.vector(c(rep("Control", times=10), rep("Tomato", times=10)))

# check and make sure it came out right
Diets.Day7.Genera
##  [1] "Control" "Control" "Control" "Control" "Control" "Control" "Control"
##  [8] "Control" "Control" "Control" "Tomato"  "Tomato"  "Tomato"  "Tomato" 
## [15] "Tomato"  "Tomato"  "Tomato"  "Tomato"  "Tomato"  "Tomato"

Run t-test

filt.Genera.Day7.ByDiet.aldex <- aldex(Day7.Counts.Genera.filt, 
                                       Diets.Day7.Genera, 
                                       mc.samples = 1000, 
                                       test = "t", 
                                       effect = TRUE)
## aldex.clr: generating Monte-Carlo instances and clr values
## operating in serial mode
## computing center with all features
## aldex.ttest: doing t-test
## aldex.effect: calculating effect sizes
filt.Genera.Day7.ByDiet.aldex <- 
  filt.Genera.Day7.ByDiet.aldex[order(filt.Genera.Day7.ByDiet.aldex$we.eBH, 
                                      decreasing = FALSE),]

kable(head(filt.Genera.Day7.ByDiet.aldex))
rab.all rab.win.Control rab.win.Tomato diff.btw diff.win effect overlap we.ep we.eBH wi.ep wi.eBH
unclassified (derived from Bacteria) 2.5026985 1.9874193 3.0144669 0.8894342 0.5458126 1.6215552 0.0466000 0.0000347 0.0256786 0.0001885 0.0816149
Staphylococcus 4.3981480 4.2392110 4.6647900 0.3930480 0.2840870 1.3716187 0.0507898 0.0003334 0.1056045 0.0002354 0.0877397
Alphatorquevirus -2.7795012 -3.9782818 -2.1901574 1.8729947 1.5103759 1.2115892 0.0950000 0.0013080 0.2092870 0.0017119 0.2325936
Lambda-like viruses -0.5334489 -1.3489239 0.2540438 1.5967109 1.1783139 1.1878787 0.1027794 0.0013951 0.2330323 0.0017119 0.2490163
Clavibacter 0.7748387 0.5505235 1.0398215 0.6786631 0.6909331 0.9901394 0.1345731 0.0034204 0.3646270 0.0059299 0.4272165
Kluyveromyces -2.7486549 -3.1193205 -2.3047942 0.8797267 0.9542796 0.8722283 0.1534000 0.0137741 0.5092141 0.0189920 0.5001200

One genera was significantly different by diet at day 7 - unclassified (derived from bacteria), padj = 0.025

hist(filt.Genera.Day7.ByDiet.aldex$we.eBH,
     breaks = 20,
     main = "Histogram of p-values on the effect of diet at day 7 on genera",
     xlab = "Benjamini Hochberg corrected p-value (we.eBH)")

What is the directionality of the change?

filt.Genera.Day7.ByDiet.aldex %>%
  select(rab.win.Control, rab.win.Tomato, we.eBH) %>%
  filter(we.eBH <= 0.05)
##                                      rab.win.Control rab.win.Tomato     we.eBH
## unclassified (derived from Bacteria)        1.987419       3.014467 0.02567862

Unclassified (derived from Bacteria) is higher in Tomato than Control.

Day 14

Look at the effect of diet on day 14.

# subset day 14 only
Day14.Counts.Genera.filt <- genera_counts_foraldex %>% 
  select(ends_with("Day14"))

ALDEx2 function needs a factor of variables

# order alphabetically so making the meta data vector is easier
Day14.Counts.Genera.filt <- Day14.Counts.Genera.filt[order(colnames(Day14.Counts.Genera.filt))]

Diets.Day14.Genera <- as.vector(c(rep("Control", times=10), rep("Tomato", times=10)))

# check and make sure it came out right
Diets.Day14.Genera
##  [1] "Control" "Control" "Control" "Control" "Control" "Control" "Control"
##  [8] "Control" "Control" "Control" "Tomato"  "Tomato"  "Tomato"  "Tomato" 
## [15] "Tomato"  "Tomato"  "Tomato"  "Tomato"  "Tomato"  "Tomato"

Run t-test

filt.Genera.Day14.ByDiet.aldex <- aldex(Day14.Counts.Genera.filt, 
                                       Diets.Day14.Genera, 
                                       mc.samples = 1000, 
                                       test = "t", 
                                       effect = TRUE)
## aldex.clr: generating Monte-Carlo instances and clr values
## operating in serial mode
## computing center with all features
## aldex.ttest: doing t-test
## aldex.effect: calculating effect sizes
filt.Genera.Day14.ByDiet.aldex <- 
  filt.Genera.Day14.ByDiet.aldex[order(filt.Genera.Day14.ByDiet.aldex$we.eBH, 
                                      decreasing = FALSE),]

kable(head(filt.Genera.Day14.ByDiet.aldex))
rab.all rab.win.Control rab.win.Tomato diff.btw diff.win effect overlap we.ep we.eBH wi.ep wi.eBH
Lambda-like viruses -0.6765788 -2.014760 1.1151072 3.1367093 0.5031233 6.152552 0.000014 0.00e+00 0.0000000 0.0000108 0.0017403
Staphylococcus 4.4868458 4.203031 4.8287360 0.6626948 0.2017804 3.372094 0.000014 0.00e+00 0.0000020 0.0000108 0.0017403
Alphatorquevirus -2.7478328 -4.775638 -0.9952775 3.7616825 1.1479595 3.213701 0.000014 1.00e-07 0.0000166 0.0000108 0.0017403
unclassified (derived from Bacteria) 2.1148119 1.507804 2.9099037 1.3704930 0.5614568 2.482695 0.000014 3.00e-07 0.0000489 0.0000108 0.0017403
Loa -3.2560289 -4.570576 -2.2214430 2.3520024 1.2624078 1.786604 0.030000 6.60e-05 0.0053620 0.0001009 0.0075989
Plasmodium -0.4456836 -1.018278 -0.0948831 0.9253498 0.4646515 1.835934 0.060188 8.46e-05 0.0068115 0.0004468 0.0195314
hist(filt.Genera.Day14.ByDiet.aldex$we.eBH,
     breaks = 20,
     main = "Histogram of p-values on the effect of diet at day 14 on genera",
     xlab = "Benjamini Hochberg corrected p-value (we.eBH)")

How many significant genera are there?

filt.Day14.Genera.aldex.sig <- filt.Genera.Day14.ByDiet.aldex[which(filt.Genera.Day14.ByDiet.aldex$we.eBH<0.05),]

length(rownames(filt.Day14.Genera.aldex.sig))
## [1] 14

Which genera are they?

sig_day14_genera_aldex2 <- as.data.frame(cbind(rownames(filt.Day14.Genera.aldex.sig),
                                 filt.Day14.Genera.aldex.sig$we.eBH))

sig_day14_genera_aldex2 <- sig_day14_genera_aldex2 %>%
  rename(Genera = V1,
         we.eBH_pvalue = V2)

sig_day14_genera_aldex2
##                                  Genera        we.eBH_pvalue
## 1                   Lambda-like viruses 3.90702968822257e-08
## 2                        Staphylococcus 1.98833990718406e-06
## 3                      Alphatorquevirus 1.66493484376377e-05
## 4  unclassified (derived from Bacteria) 4.88751914488331e-05
## 5                                   Loa  0.00536199352280373
## 6                            Plasmodium  0.00681151568341803
## 7                     Propionibacterium  0.00938576734150738
## 8                         Saccharomyces   0.0162702517857512
## 9                      Stenotrophomonas   0.0215525889312409
## 10                           Malassezia   0.0222884879030356
## 11                          Roseiflexus   0.0224147224012945
## 12                               Brugia   0.0315547210562422
## 13                        Streptococcus   0.0316064329890332
## 14                      Vanderwaltozyma   0.0355364448144129
  • Lambda-like viruses
  • Staphylococcus
  • Alphatorquervirus
  • unclassified (derived from Bacteria)
  • Loa
  • Plasmodium
  • Propionibacterium
  • Saccharomyces, Stenotrophomonas
  • Malassezia
  • Roseiflexus
  • Brugia
  • Strepococcus
  • Vanderwaltozyma

What is the directionality of the change?

filt.Genera.Day14.ByDiet.aldex %>%
  select(rab.win.Control, rab.win.Tomato, we.eBH) %>%
  filter(we.eBH <= 0.05)
##                                      rab.win.Control rab.win.Tomato
## Lambda-like viruses                      -2.01475973      1.1151072
## Staphylococcus                            4.20303084      4.8287360
## Alphatorquevirus                         -4.77563841     -0.9952775
## unclassified (derived from Bacteria)      1.50780388      2.9099037
## Loa                                      -4.57057605     -2.2214430
## Plasmodium                               -1.01827823     -0.0948831
## Propionibacterium                         1.40929760      2.0214784
## Saccharomyces                            -1.92007049      0.3880179
## Stenotrophomonas                          0.01556489      0.4808232
## Malassezia                               -3.61348687     -1.7990132
## Roseiflexus                               2.61037473      2.3131767
## Brugia                                   -3.16504650     -1.6096471
## Streptococcus                            10.54449623      8.0218160
## Vanderwaltozyma                          -4.08112899     -1.1653644
##                                            we.eBH
## Lambda-like viruses                  3.907030e-08
## Staphylococcus                       1.988340e-06
## Alphatorquevirus                     1.664935e-05
## unclassified (derived from Bacteria) 4.887519e-05
## Loa                                  5.361994e-03
## Plasmodium                           6.811516e-03
## Propionibacterium                    9.385767e-03
## Saccharomyces                        1.627025e-02
## Stenotrophomonas                     2.155259e-02
## Malassezia                           2.228849e-02
## Roseiflexus                          2.241472e-02
## Brugia                               3.155472e-02
## Streptococcus                        3.160643e-02
## Vanderwaltozyma                      3.553644e-02

All significantly different genera are higher in tomato as compared to control.

Subset by diet

Control

# subset control only samples across all time points, should be n=30
Control.Counts.Genera.filt <- genera_counts_foraldex %>% 
  select(contains("Control"))

dim(Control.Counts.Genera.filt)
## [1] 755  30

ALDEx2 function needs a factor of variables

# results in pigs at different time points being grouped together
Control.Counts.Genera.filt <- Control.Counts.Genera.filt[order(colnames(Control.Counts.Genera.filt))]

# then time point by "alphabetical" where 14 comes before 7
# ex, first few are Pig 10 Day 0, Pig 10 Day 14, Pig 10 Day 7, Pig 1 Day 0, Pig 1 Day 14, etc
TimePoints.Control.Genera <- as.vector(rep(c("Day0", "Day14", "Day7"), times=10))

# check and make sure it looks right
TimePoints.Control.Genera
##  [1] "Day0"  "Day14" "Day7"  "Day0"  "Day14" "Day7"  "Day0"  "Day14" "Day7" 
## [10] "Day0"  "Day14" "Day7"  "Day0"  "Day14" "Day7"  "Day0"  "Day14" "Day7" 
## [19] "Day0"  "Day14" "Day7"  "Day0"  "Day14" "Day7"  "Day0"  "Day14" "Day7" 
## [28] "Day0"  "Day14" "Day7"

More than two conditions this time, use the ANOVA-like test, Kruskal Wallis

filt.Genera.Control.ByTime.aldex <- aldex(Control.Counts.Genera.filt, 
                                          TimePoints.Control.Genera, 
                                          mc.samples = 1000, 
                                          test = "kw", 
                                          effect = FALSE)
## aldex.clr: generating Monte-Carlo instances and clr values
## operating in serial mode
## computing center with all features
## aldex.glm: doing Kruskal-Wallace and glm test (ANOVA-like)
## operating in serial mode

We are looking at glm.eBH for the BH corrected ANOVA pvalue

filt.Genera.Control.ByTime.aldex <- 
  filt.Genera.Control.ByTime.aldex[order(filt.Genera.Control.ByTime.aldex$glm.eBH, 
                                         decreasing = FALSE),]

kable(head(filt.Genera.Control.ByTime.aldex))
kw.ep kw.eBH glm.ep glm.eBH
Oribacterium 0.0005354 0.1104829 0.0000000 0.0000158
Streptococcus 0.0000760 0.0558923 0.0000000 0.0000159
Lactococcus 0.0004159 0.1045718 0.0000065 0.0015823
Granulicatella 0.0007024 0.1276212 0.0000951 0.0149421
T4-like viruses 0.0056658 0.3088761 0.0011659 0.0822780
Schizosaccharomyces 0.0094127 0.3725284 0.0026917 0.1301247
hist(filt.Genera.Control.ByTime.aldex$glm.eBH,
     breaks = 20,
     main = "Histogram of p-values on the effect of time within the control diet on genera",
     xlab = "Benjamini Hochberg corrected p-value (glm.eBH)")

How many significantly different genera are there?

filt.Genera.Control.ByTime.aldex.sig <- 
  filt.Genera.Control.ByTime.aldex[which(filt.Genera.Control.ByTime.aldex$glm.eBH<0.05),]

length(rownames(filt.Genera.Control.ByTime.aldex.sig))
## [1] 4

4 sig genera

Which genera are they?

sig_control_genera_aldex2 <- as.data.frame(cbind(rownames(filt.Genera.Control.ByTime.aldex.sig),
                                 filt.Genera.Control.ByTime.aldex.sig$glm.eBH))

sig_control_genera_aldex2 <- sig_control_genera_aldex2 %>%
  rename(Genera = V1,
         glm.eBH_pval = V2)

sig_control_genera_aldex2
##           Genera         glm.eBH_pval
## 1   Oribacterium 1.58068029273393e-05
## 2  Streptococcus 1.58746766478819e-05
## 3    Lactococcus  0.00158226394942239
## 4 Granulicatella   0.0149420529335598
  • Oribacterium
  • Streptococcus
  • Lactococcus
  • Granulicatella

Tomato

# subset tomato only samples across all time points, should be n=30
Tomato.Counts.Genera.filt <- genera_counts_foraldex %>% 
  select(contains("Tomato"))

ALDEx2 function needs a factor of variables

# results in pigs at different time points being grouped together
Tomato.Counts.Genera.filt <- Tomato.Counts.Genera.filt[order(colnames(Tomato.Counts.Genera.filt))]

# then time point by "alphabetical" where 14 comes before 7
# ex, first few are Pig 10 Day 0, Pig 10 Day 14, Pig 10 Day 7, Pig 1 Day 0, Pig 1 Day 14, etc
TimePoints.Tomato.Genera <- as.vector(rep(c("Day0", "Day14", "Day7"), times=10))

# check and make sure it looks right
TimePoints.Tomato.Genera
##  [1] "Day0"  "Day14" "Day7"  "Day0"  "Day14" "Day7"  "Day0"  "Day14" "Day7" 
## [10] "Day0"  "Day14" "Day7"  "Day0"  "Day14" "Day7"  "Day0"  "Day14" "Day7" 
## [19] "Day0"  "Day14" "Day7"  "Day0"  "Day14" "Day7"  "Day0"  "Day14" "Day7" 
## [28] "Day0"  "Day14" "Day7"

More than two conditions this time, use the ANOVA-like test

filt.Genera.Tomato.ByTime.aldex <- aldex(Tomato.Counts.Genera.filt, 
                                          TimePoints.Tomato.Genera, 
                                          mc.samples = 1000, 
                                          test = "kw", 
                                          effect = FALSE)
## aldex.clr: generating Monte-Carlo instances and clr values
## operating in serial mode
## computing center with all features
## aldex.glm: doing Kruskal-Wallace and glm test (ANOVA-like)
## operating in serial mode

We are looking at glm.eBH for the BH corrected ANOVA pvalue

filt.Genera.Tomato.ByTime.aldex <- 
  filt.Genera.Tomato.ByTime.aldex[order(filt.Genera.Tomato.ByTime.aldex$glm.eBH, 
                                         decreasing = FALSE),]

kable(head(filt.Genera.Tomato.ByTime.aldex))
kw.ep kw.eBH glm.ep glm.eBH
Staphylococcus 0.0000565 0.0342115 0.0000000 0.0000001
Alphatorquevirus 0.0001143 0.0390495 0.0000003 0.0000802
Lambda-like viruses 0.0002278 0.0582011 0.0000017 0.0004113
unclassified (derived from Bacteria) 0.0019396 0.2946385 0.0000066 0.0012508
Streptococcus 0.0025595 0.3275011 0.0014981 0.1707533
Crocosphaera 0.0117640 0.4694176 0.0104829 0.3025469
hist(filt.Genera.Tomato.ByTime.aldex$glm.eBH,
     breaks = 20,
     main = "Histogram of p-values on the effect of time within the tomato diet on genera",
     xlab = "Benjamini Hochberg corrected p-value (glm.eBH)")

How many significantly different genera are there?

filt.Genera.Tomato.ByTime.aldex.sig <- 
  filt.Genera.Tomato.ByTime.aldex[which(filt.Genera.Tomato.ByTime.aldex$glm.eBH<0.05),]

length(rownames(filt.Genera.Tomato.ByTime.aldex.sig))
## [1] 4

4 sig genera

Which genera are they?

sig_tomato_genera_aldex2 <- as.data.frame(cbind(rownames(filt.Genera.Tomato.ByTime.aldex.sig),
                                 filt.Genera.Tomato.ByTime.aldex.sig$glm.eBH))

sig_tomato_genera_aldex2 <- sig_tomato_genera_aldex2 %>%
  rename(Genera = V1,
         glm.eBH_pval = V2)

sig_tomato_genera_aldex2
##                                 Genera         glm.eBH_pval
## 1                       Staphylococcus 1.09222660374843e-07
## 2                     Alphatorquevirus 8.01595357034658e-05
## 3                  Lambda-like viruses 0.000411303309560209
## 4 unclassified (derived from Bacteria)  0.00125079010124612
  • Staphylococcus
  • Alphatorquevirus
  • Lambda-like viruses
  • unclassified (derived from Bacteria)

Any overlap between sig differences at day 14 and by diet?

Control over time and day 14 overlap

intersect(sig_day14_genera_aldex2$Genera, sig_control_genera_aldex2$Genera)
## [1] "Streptococcus"

Streptococcus

Tomato over time and day 14 overlap

intersect(sig_day14_genera_aldex2$Genera, sig_tomato_genera_aldex2$Genera)
## [1] "Lambda-like viruses"                 
## [2] "Staphylococcus"                      
## [3] "Alphatorquevirus"                    
## [4] "unclassified (derived from Bacteria)"
  • Lambda-like viruses
  • Staphylococcus
  • Alphatorquevirus
  • unclassified (derived from Bacteria)

Phyla-level annotation

Read in phyla level data, annotated from MG-RAST. In “Phyla” tab of Supplementary Information.

Phyla.Counts <- read_excel("../Goggans_etal_2021_tomato_pig_microbiome_WGS.xlsx",
                                       sheet = "TableS3.Phyla")

str(Phyla.Counts)
## tibble [60 × 62] (S3: tbl_df/tbl/data.frame)
##  $ domain                                    : chr [1:60] "Bacteria" "Bacteria" "Eukaryota" "Bacteria" ...
##  $ phylum                                    : chr [1:60] "Acidobacteria" "Actinobacteria" "Apicomplexa" "Aquificae" ...
##  $ ShotgunWGS-ControlPig6GutMicrobiome-Day14 : num [1:60] 2874 186789 231 1953 368 ...
##  $ ShotgunWGS-ControlPig8GutMicrobiome-Day0  : num [1:60] 3717 277130 384 2254 992 ...
##  $ ShotgunWGS-ControlPig3GutMicrobiome-Day14 : num [1:60] 2663 126155 190 1642 386 ...
##  $ ShotgunWGS-TomatoPig14GutMicrobiome-Day7  : num [1:60] 880 39557 168 647 211 ...
##  $ ShotgunWGS-ControlPig5GutMicrobiome-Day7  : num [1:60] 2016 142345 171 1418 400 ...
##  $ ShotgunWGS-TomatoPig18GutMicrobiome-Day7  : num [1:60] 1377 181295 101 658 201 ...
##  $ ShotgunWGS-TomatoPig16GutMicrobiome-Day7  : num [1:60] 1570 58263 224 892 340 ...
##  $ ShotgunWGS-ControlPig10GutMicrobiome-Day7 : num [1:60] 1298 109273 287 900 211 ...
##  $ ShotgunWGS-ControlPig2GutMicrobiome-Day0  : num [1:60] 3114 159425 529 2095 764 ...
##  $ ShotgunWGS-TomatoPig18GutMicrobiome-Day0  : num [1:60] 2604 168472 170 1422 393 ...
##  $ ShotgunWGS-ControlPig10GutMicrobiome-Day0 : num [1:60] 3118 163425 231 1192 426 ...
##  $ ShotgunWGS-ControlPig7GutMicrobiome-Day0  : num [1:60] 2796 70967 389 1137 605 ...
##  $ ShotgunWGS-ControlPig8GutMicrobiome-Day14 : num [1:60] 2222 91465 325 1561 364 ...
##  $ ShotgunWGS-TomatoPig11GutMicrobiome-Day0  : num [1:60] 2136 68481 402 1377 691 ...
##  $ ShotgunWGS-TomatoPig19GutMicrobiome-Day0  : num [1:60] 2017 207693 143 1265 361 ...
##  $ ShotgunWGS-TomatoPig17GutMicrobiome-Day14 : num [1:60] 836 26050 147 633 212 ...
##  $ ShotgunWGS-ControlPig9GutMicrobiome-Day14 : num [1:60] 2612 172091 181 1645 393 ...
##  $ ShotgunWGS-ControlPig10GutMicrobiome-Day14: num [1:60] 2136 122681 250 1536 445 ...
##  $ ShotgunWGS-TomatoPig19GutMicrobiome-Day7  : num [1:60] 1090 78218 168 774 304 ...
##  $ ShotgunWGS-ControlPig5GutMicrobiome-Day14 : num [1:60] 2693 263950 266 1713 577 ...
##  $ ShotgunWGS-ControlPig2GutMicrobiome-Day7  : num [1:60] 3420 101192 582 2369 766 ...
##  $ ShotgunWGS-ControlPig6GutMicrobiome-Day7  : num [1:60] 2216 159323 115 1383 303 ...
##  $ ShotgunWGS-TomatoPig12GutMicrobiome-Day0  : num [1:60] 2146 78205 221 1265 390 ...
##  $ ShotgunWGS-TomatoPig14GutMicrobiome-Day0  : num [1:60] 732 77377 292 585 223 ...
##  $ ShotgunWGS-ControlPig7GutMicrobiome-Day14 : num [1:60] 2079 142139 322 1335 392 ...
##  $ ShotgunWGS-TomatoPig11GutMicrobiome-Day14 : num [1:60] 570 25927 180 425 270 ...
##  $ ShotgunWGS-TomatoPig20GutMicrobiome-Day0  : num [1:60] 2472 82091 415 1534 647 ...
##  $ ShotgunWGS-ControlPig9GutMicrobiome-Day0  : num [1:60] 1607 88397 432 1085 423 ...
##  $ ShotgunWGS-TomatoPig11GutMicrobiome-Day7  : num [1:60] 278 17451 96 150 107 ...
##  $ ShotgunWGS-TomatoPig13GutMicrobiome-Day7  : num [1:60] 1100 56205 157 984 306 ...
##  $ ShotgunWGS-TomatoPig17GutMicrobiome-Day0  : num [1:60] 1562 74553 171 780 238 ...
##  $ ShotgunWGS-TomatoPig19GutMicrobiome-Day14 : num [1:60] 765 47957 182 551 237 ...
##  $ ShotgunWGS-TomatoPig13GutMicrobiome-Day0  : num [1:60] 2182 124473 280 1483 476 ...
##  $ ShotgunWGS-ControlPig2GutMicrobiome-Day14 : num [1:60] 3329 116448 325 2149 703 ...
##  $ ShotgunWGS-ControlPig1GutMicrobiome-Day7  : num [1:60] 1920 55849 156 1234 408 ...
##  $ ShotgunWGS-TomatoPig15GutMicrobiome-Day7  : num [1:60] 757 38904 204 583 317 ...
##  $ ShotgunWGS-TomatoPig15GutMicrobiome-Day0  : num [1:60] 2037 120272 320 1399 561 ...
##  $ ShotgunWGS-TomatoPig12GutMicrobiome-Day7  : num [1:60] 1279 87121 215 917 409 ...
##  $ ShotgunWGS-TomatoPig14GutMicrobiome-Day14 : num [1:60] 583 36948 69 444 102 ...
##  $ ShotgunWGS-TomatoPig20GutMicrobiome-Day14 : num [1:60] 496 29179 99 374 142 ...
##  $ ShotgunWGS-ControlPig1GutMicrobiome-Day0  : num [1:60] 2963 90535 278 1596 631 ...
##  $ ShotgunWGS-ControlPig4GutMicrobiome-Day14 : num [1:60] 2548 133556 181 1734 432 ...
##  $ ShotgunWGS-ControlPig6GutMicrobiome-Day0  : num [1:60] 2269 127508 314 1058 413 ...
##  $ ShotgunWGS-TomatoPig16GutMicrobiome-Day0  : num [1:60] 1935 110140 207 1018 425 ...
##  $ ShotgunWGS-TomatoPig16GutMicrobiome-Day14 : num [1:60] 817 33981 133 443 187 ...
##  $ ShotgunWGS-TomatoPig18GutMicrobiome-Day14 : num [1:60] 705 92977 122 507 148 ...
##  $ ShotgunWGS-ControlPig7GutMicrobiome-Day7  : num [1:60] 1131 69602 297 628 290 ...
##  $ ShotgunWGS-ControlPig4GutMicrobiome-Day7  : num [1:60] 1298 112714 203 983 325 ...
##  $ ShotgunWGS-TomatoPig13GutMicrobiome-Day14 : num [1:60] 566 36447 72 514 125 ...
##  $ ShotgunWGS-ControlPig8GutMicrobiome-Day7  : num [1:60] 2173 159187 378 1311 361 ...
##  $ ShotgunWGS-TomatoPig15GutMicrobiome-Day14 : num [1:60] 1186 49134 150 858 249 ...
##  $ ShotgunWGS-TomatoPig12GutMicrobiome-Day14 : num [1:60] 1122 64744 254 1030 427 ...
##  $ ShotgunWGS-TomatoPig20GutMicrobiome-Day7  : num [1:60] 1109 97728 149 670 211 ...
##  $ ShotgunWGS-ControlPig1GutMicrobiome-Day14 : num [1:60] 2350 83993 210 1719 446 ...
##  $ ShotgunWGS-ControlPig3GutMicrobiome-Day0  : num [1:60] 3314 428097 206 2366 519 ...
##  $ ShotgunWGS-ControlPig5GutMicrobiome-Day0  : num [1:60] 2998 242356 283 1895 758 ...
##  $ ShotgunWGS-ControlPig4GutMicrobiome-Day0  : num [1:60] 3042 223010 351 1777 685 ...
##  $ ShotgunWGS-ControlPig9GutMicrobiome-Day7  : num [1:60] 499 68424 784 329 171 ...
##  $ ShotgunWGS-ControlPig3GutMicrobiome-Day7  : num [1:60] 2620 340300 165 1993 484 ...
##  $ ShotgunWGS-TomatoPig17GutMicrobime-Day7   : num [1:60] 1340 71395 159 648 270 ...

Data filtering

Remove inplausible phyla

These phyla are not plausibly found in a rectal swab of a pig, and were incorrectly annotated, so we are removing them.

Phyla.Counts.Filt <- Phyla.Counts %>%
  filter(phylum != "Chordata" , phylum != "Arthropoda" , phylum != "Cnidaria" , 
         phylum != "Porifera" , phylum != "Echinodermata", phylum != "Streptophyta",
         phylum != "Platyhelminthes")

Transpose.

Phyla.Counts.Filt.t <- as.tibble(t(Phyla.Counts.Filt))

# make phyla colnames
colnames(Phyla.Counts.Filt.t) <- Phyla.Counts.Filt.t[2,]

# remove domain, phylum rows
Phyla.Counts.Filt.t <- Phyla.Counts.Filt.t[3:62,]

# convert character to numeric
Phyla.Counts.Filt.t <- as.data.frame(apply((Phyla.Counts.Filt.t), 2, as.numeric))

str(Phyla.Counts.Filt.t[,1:5])
## 'data.frame':    60 obs. of  5 variables:
##  $ Acidobacteria : num  2874 3717 2663 880 2016 ...
##  $ Actinobacteria: num  186789 277130 126155 39557 142345 ...
##  $ Apicomplexa   : num  231 384 190 168 171 101 224 287 529 170 ...
##  $ Aquificae     : num  1953 2254 1642 647 1418 ...
##  $ Ascomycota    : num  1491 2196 1281 672 1178 ...
# add back sample names as column
Phyla.Counts.Filt.t <- Phyla.Counts.Filt.t %>%
  mutate(Sample_Name = AllSamples.Metadata$Sample_Name)

# move Sample_Name to first column
Phyla.Counts.Filt.t <- Phyla.Counts.Filt.t %>%
  relocate(Sample_Name)

kable(head(Phyla.Counts.Filt.t))
Sample_Name Acidobacteria Actinobacteria Apicomplexa Aquificae Ascomycota Bacillariophyta Bacteroidetes Basidiomycota Blastocladiomycota Candidatus Poribacteria Chlamydiae Chlorobi Chloroflexi Chlorophyta Chromerida Chrysiogenetes Chytridiomycota Crenarchaeota Cyanobacteria Deferribacteres Deinococcus-Thermus Dictyoglomi Elusimicrobia Euglenida Euryarchaeota Fibrobacteres Firmicutes Fusobacteria Gemmatimonadetes Glomeromycota Hemichordata Korarchaeota Lentisphaerae Microsporidia Nanoarchaeota Nematoda Nitrospirae Phaeophyceae Placozoa Planctomycetes Proteobacteria Spirochaetes Synergistetes Tenericutes Thaumarchaeota Thermotogae Verrucomicrobia Xanthophyceae unclassified (derived from Bacteria) unclassified (derived from Eukaryota) unclassified (derived from Fungi) unclassified (derived from Viruses) unclassified (derived from other sequences)
ShotgunWGS-ControlPig6GutMicrobiome-Day14 2874 186789 231 1953 1491 105 1424565 240 0 26 552 4889 7842 370 0 331 0 648 8838 1494 2481 1217 632 3 13175 4768 2059948 15350 211 0 26 75 765 49 4 178 551 0 68 1523 105309 11519 4453 2764 56 5014 3209 1 1197 1260 0 1546 48
ShotgunWGS-ControlPig8GutMicrobiome-Day0 3717 277130 384 2254 2196 184 1391417 405 0 49 829 6073 9612 571 0 416 0 902 13612 1994 3586 1554 1007 0 19176 6963 2223331 21242 340 0 18 97 1833 88 5 265 797 1 68 2632 154698 17463 7489 3731 80 7105 6282 1 1720 4189 1 2626 33
ShotgunWGS-ControlPig3GutMicrobiome-Day14 2663 126155 190 1642 1281 129 1260217 198 0 21 554 4469 7596 362 0 271 0 638 11276 1419 2321 1109 699 0 12790 4985 2266610 14356 241 0 16 52 774 56 4 146 563 2 31 1570 104879 10922 4148 2493 59 5052 3738 0 1148 1266 0 2003 62
ShotgunWGS-TomatoPig14GutMicrobiome-Day7 880 39557 168 647 672 65 415935 114 0 17 223 1565 2849 184 0 114 2 328 3426 543 967 424 359 1 8299 1750 628580 5545 61 0 5 36 492 24 6 138 241 1 19 540 71783 5634 1870 1292 37 1998 1259 0 1161 662 6 1010 115
ShotgunWGS-ControlPig5GutMicrobiome-Day7 2016 142345 171 1418 1178 111 798569 182 1 22 505 3835 6249 388 0 273 0 652 10511 1166 2095 897 665 2 13289 4040 1919749 13260 211 0 8 66 1339 80 4 161 483 1 27 1674 114187 10757 4358 2309 55 4527 3442 1 1323 1369 0 1475 13
ShotgunWGS-TomatoPig18GutMicrobiome-Day7 1377 181295 101 658 799 64 690378 119 0 17 222 2198 3255 220 0 91 0 274 7460 445 1102 423 230 0 4970 2071 793943 5068 208 0 2 25 288 32 1 134 245 0 29 1096 69391 4728 1482 915 26 1710 2361 0 1431 518 0 1267 59

Calculate relative abundance, and bind back to metadata.

Phyla.Counts.Filt.t.wtotal <- Phyla.Counts.Filt.t %>%
  mutate(Total.Counts = rowSums(Phyla.Counts.Filt.t[,2:ncol(Phyla.Counts.Filt.t)]))

dim(Phyla.Counts.Filt.t.wtotal)
## [1] 60 55
# create rel abund df
RelAbund.Phyla.Filt <- Phyla.Counts.Filt.t.wtotal[,2:54]/Phyla.Counts.Filt.t.wtotal$Total.Counts

# add back metadata
RelAbund.Phyla.Filt <- bind_cols(AllSamples.Metadata, RelAbund.Phyla.Filt)

Counting missing data

# remove metadata
RelAbund.Phyla.Filt.nometadata <- RelAbund.Phyla.Filt %>%
  select_if(is.numeric) 

# create a list with the number of zeros for each genus
counting_zeros_phyla <- sapply(RelAbund.Phyla.Filt.nometadata, function(x){ (sum(x==0))})

# plot a histogram to look
counting_zeros_phyla_df <- as.data.frame(counting_zeros_phyla)

hist(counting_zeros_phyla_df$counting_zeros_phyla, 
     breaks = 61,
     main = "Histogram of Genera with Zero Relative Intensity",
     sub = "Starting at No Zeros",
     xlab = "Number of zero relative intensity values",
     ylab = "Frequency")

Big first bar is many phyla which have zero missing values.

# filter for any phyla with at least 1 missing value
counting_zeros_phyla_df_missingval <- counting_zeros_phyla_df %>%
  rownames_to_column(var = "rowname") %>%
  filter(counting_zeros_phyla > 0) %>%
  column_to_rownames(var = "rowname")

# how many genera have at least one missing value?
dim(counting_zeros_phyla_df_missingval)
## [1] 9 1

9 phyla have at least 1 missing value.

# histogram of number of zeros, starting at 1 zero
hist(counting_zeros_phyla_df_missingval$counting_zeros_phyla, 
     breaks = 60,
     main = "Histogram of Genera with Zero Relative Intensity",
     sub = "Starting at 1 Zero",
     xlab = "Number of zero relative intensity values",
     ylab = "Frequency")

# create table of number of phyla with more than 1 missing value
counting_zeros_phyla_df_missingval
##                                   counting_zeros_phyla
## Blastocladiomycota                                  55
## Chromerida                                          48
## Chytridiomycota                                     42
## Euglenida                                           31
## Glomeromycota                                       59
## Nanoarchaeota                                        8
## Phaeophyceae                                        35
## Xanthophyceae                                       29
## unclassified (derived from Fungi)                   46

Filter for <33% missingness

This would mean 33% missing values in our dataset.

# removing phyla that have 20 or more zeros
counting_zeros_phyla_df_missing20ormore <- counting_zeros_phyla_df %>%
  rownames_to_column(var = "rowname") %>%
  filter(counting_zeros_phyla >= 20) %>%
  column_to_rownames(var = "rowname")

# how many phyla have 20 or more missing value?
dim(counting_zeros_phyla_df_missing20ormore)
## [1] 8 1

8 phyla have more than 20 missing values.

# make a character vector from the rownames of previous data frame containing the phyla we want to get rid of
phyla.20zeros <- c(rownames(counting_zeros_phyla_df_missing20ormore))

# use select function to select all columns EXCEPT the ones in the character vector, we want to remove those
# and add in metadata
RelAbund.Phyla.Filt.zerofilt <- RelAbund.Phyla.Filt %>%
  select(everything(), -all_of(phyla.20zeros))

# check dimensions to make sure it filtered correctly
dim(RelAbund.Phyla.Filt.zerofilt)
## [1] 60 50
# removed 8, like we expected

Our final dataset has 45 phyla (because 5 columns are metadata).

Write final dataset genus rel abund to csv

write_csv(RelAbund.Phyla.Filt.zerofilt,
          file = "Phyla_RelAbund_Final_Filtered_WithMetadata.csv")

Microbiome profile

See “Genera” section above for rarefaction curves and kronas plots

Wrangling

Wrangling to enable collection of some summary statistics about our microbiome profile.

Grab names of final phyla

# contains inplausible genera removed, but not removed for zeroes
dim(Phyla.Counts.Filt)
## [1] 53 62
Phyla.Counts.Filt[1:10, 1:5]
## # A tibble: 10 × 5
##    domain    phylum           `ShotgunWGS-Co…` `ShotgunWGS-Co…` `ShotgunWGS-Co…`
##    <chr>     <chr>                       <dbl>            <dbl>            <dbl>
##  1 Bacteria  Acidobacteria                2874             3717             2663
##  2 Bacteria  Actinobacteria             186789           277130           126155
##  3 Eukaryota Apicomplexa                   231              384              190
##  4 Bacteria  Aquificae                    1953             2254             1642
##  5 Eukaryota Ascomycota                   1491             2196             1281
##  6 Eukaryota Bacillariophyta               105              184              129
##  7 Bacteria  Bacteroidetes             1424565          1391417          1260217
##  8 Eukaryota Basidiomycota                 240              405              198
##  9 Eukaryota Blastocladiomyc…                0                0                0
## 10 Bacteria  Candidatus Pori…               26               49               21
# final filtered data
RelAbund.Phyla.Filt.zerofilt[1:10, 1:5]
## # A tibble: 10 × 5
##    Sample_Name                           Pig   Diet  Time_Point Diet_By_Time_Po…
##    <chr>                                 <fct> <fct> <fct>      <fct>           
##  1 ShotgunWGS-ControlPig6GutMicrobiome-… 6     Cont… Day 14     Control Day 14  
##  2 ShotgunWGS-ControlPig8GutMicrobiome-… 8     Cont… Day 0      Control Day 0   
##  3 ShotgunWGS-ControlPig3GutMicrobiome-… 3     Cont… Day 14     Control Day 14  
##  4 ShotgunWGS-TomatoPig14GutMicrobiome-… 14    Toma… Day 7      Tomato Day 7    
##  5 ShotgunWGS-ControlPig5GutMicrobiome-… 5     Cont… Day 7      Control Day 7   
##  6 ShotgunWGS-TomatoPig18GutMicrobiome-… 18    Toma… Day 7      Tomato Day 7    
##  7 ShotgunWGS-TomatoPig16GutMicrobiome-… 16    Toma… Day 7      Tomato Day 7    
##  8 ShotgunWGS-ControlPig10GutMicrobiome… 10    Cont… Day 7      Control Day 7   
##  9 ShotgunWGS-ControlPig2GutMicrobiome-… 2     Cont… Day 0      Control Day 0   
## 10 ShotgunWGS-TomatoPig18GutMicrobiome-… 18    Toma… Day 0      Tomato Day 0
dim(RelAbund.Phyla.Filt.zerofilt)
## [1] 60 50
# grab colnames which have all the final phyla
final_phyla <- colnames(RelAbund.Phyla.Filt.zerofilt)

final_phyla
##  [1] "Sample_Name"                                
##  [2] "Pig"                                        
##  [3] "Diet"                                       
##  [4] "Time_Point"                                 
##  [5] "Diet_By_Time_Point"                         
##  [6] "Acidobacteria"                              
##  [7] "Actinobacteria"                             
##  [8] "Apicomplexa"                                
##  [9] "Aquificae"                                  
## [10] "Ascomycota"                                 
## [11] "Bacillariophyta"                            
## [12] "Bacteroidetes"                              
## [13] "Basidiomycota"                              
## [14] "Candidatus Poribacteria"                    
## [15] "Chlamydiae"                                 
## [16] "Chlorobi"                                   
## [17] "Chloroflexi"                                
## [18] "Chlorophyta"                                
## [19] "Chrysiogenetes"                             
## [20] "Crenarchaeota"                              
## [21] "Cyanobacteria"                              
## [22] "Deferribacteres"                            
## [23] "Deinococcus-Thermus"                        
## [24] "Dictyoglomi"                                
## [25] "Elusimicrobia"                              
## [26] "Euryarchaeota"                              
## [27] "Fibrobacteres"                              
## [28] "Firmicutes"                                 
## [29] "Fusobacteria"                               
## [30] "Gemmatimonadetes"                           
## [31] "Hemichordata"                               
## [32] "Korarchaeota"                               
## [33] "Lentisphaerae"                              
## [34] "Microsporidia"                              
## [35] "Nanoarchaeota"                              
## [36] "Nematoda"                                   
## [37] "Nitrospirae"                                
## [38] "Placozoa"                                   
## [39] "Planctomycetes"                             
## [40] "Proteobacteria"                             
## [41] "Spirochaetes"                               
## [42] "Synergistetes"                              
## [43] "Tenericutes"                                
## [44] "Thaumarchaeota"                             
## [45] "Thermotogae"                                
## [46] "Verrucomicrobia"                            
## [47] "unclassified (derived from Bacteria)"       
## [48] "unclassified (derived from Eukaryota)"      
## [49] "unclassified (derived from Viruses)"        
## [50] "unclassified (derived from other sequences)"
# remove metadata colnames
final_phyla <- final_phyla[6:50]  

final_phyla <- as.data.frame(final_phyla)

final_phyla <- final_phyla %>%
  rename(phylum = final_phyla)

Get back domain and inner_join with final_phyla list

# pull from full dataset the domain and genus columns
Phyla.Counts.Filt.Domain.Phyla <- Phyla.Counts.Filt %>%
  select(domain, phylum)

head(Phyla.Counts.Filt.Domain.Phyla)
## # A tibble: 6 × 2
##   domain    phylum         
##   <chr>     <chr>          
## 1 Bacteria  Acidobacteria  
## 2 Bacteria  Actinobacteria 
## 3 Eukaryota Apicomplexa    
## 4 Bacteria  Aquificae      
## 5 Eukaryota Ascomycota     
## 6 Eukaryota Bacillariophyta
# want to join Genus.Counts.Filt.Domain.Genera with final_phyla
final_phyla_withdomain <- inner_join(final_phyla, Phyla.Counts.Filt.Domain.Phyla,
                                     by = "phylum")

Count phyla

final_phyla_withdomain %>%
  count()
##    n
## 1 45
final_phyla_withdomain %>%
  group_by(domain) %>%
  count()
## # A tibble: 5 × 2
## # Groups:   domain [5]
##   domain              n
##   <chr>           <int>
## 1 Archaea             5
## 2 Bacteria           28
## 3 Eukaryota          10
## 4 other sequences     1
## 5 Viruses             1

Most prevalent phyla

RelAbund.Phyla.Filt.zerofilt[1:5, 1:10]
## # A tibble: 5 × 10
##   Sample_Name              Pig   Diet  Time_Point Diet_By_Time_Po… Acidobacteria
##   <chr>                    <fct> <fct> <fct>      <fct>                    <dbl>
## 1 ShotgunWGS-ControlPig6G… 6     Cont… Day 14     Control Day 14        0.000741
## 2 ShotgunWGS-ControlPig8G… 8     Cont… Day 0      Control Day 0         0.000885
## 3 ShotgunWGS-ControlPig3G… 3     Cont… Day 14     Control Day 14        0.000690
## 4 ShotgunWGS-TomatoPig14G… 14    Toma… Day 7      Tomato Day 7          0.000732
## 5 ShotgunWGS-ControlPig5G… 5     Cont… Day 7      Control Day 7         0.000656
## # … with 4 more variables: Actinobacteria <dbl>, Apicomplexa <dbl>,
## #   Aquificae <dbl>, Ascomycota <dbl>
phyla_means <- RelAbund.Phyla.Filt.zerofilt %>%
  summarize_if(is.numeric, mean)

phyla_means_t <- t(phyla_means)
phyla_means_t <- as.data.frame(phyla_means_t)

phyla_means_t %>%
  rename(rel_abund_phyla = V1) %>%
  arrange(-rel_abund_phyla)
##                                             rel_abund_phyla
## Firmicutes                                     5.273546e-01
## Bacteroidetes                                  3.544950e-01
## Actinobacteria                                 4.660132e-02
## Proteobacteria                                 3.859541e-02
## Fusobacteria                                   4.300028e-03
## Euryarchaeota                                  4.280344e-03
## Spirochaetes                                   3.786741e-03
## Cyanobacteria                                  2.973060e-03
## Chloroflexi                                    2.143931e-03
## Fibrobacteres                                  1.647436e-03
## Thermotogae                                    1.475999e-03
## Synergistetes                                  1.429229e-03
## Chlorobi                                       1.371726e-03
## Verrucomicrobia                                1.113246e-03
## Tenericutes                                    8.325538e-04
## unclassified (derived from Viruses)            7.714176e-04
## Acidobacteria                                  7.491839e-04
## Deinococcus-Thermus                            7.206358e-04
## unclassified (derived from Eukaryota)          5.727411e-04
## unclassified (derived from Bacteria)           5.606341e-04
## Ascomycota                                     5.224676e-04
## Planctomycetes                                 5.021699e-04
## Aquificae                                      4.911229e-04
## Deferribacteres                                4.032208e-04
## Lentisphaerae                                  3.300053e-04
## Chlamydiae                                     3.262732e-04
## Dictyoglomi                                    3.130261e-04
## Elusimicrobia                                  2.375048e-04
## Crenarchaeota                                  2.006612e-04
## Nitrospirae                                    1.656603e-04
## Chlorophyta                                    1.318528e-04
## Apicomplexa                                    1.227757e-04
## Basidiomycota                                  8.845319e-05
## Nematoda                                       8.275259e-05
## Chrysiogenetes                                 8.110410e-05
## Gemmatimonadetes                               6.650590e-05
## Bacillariophyta                                3.943884e-05
## unclassified (derived from other sequences)    2.783963e-05
## Microsporidia                                  2.109075e-05
## Thaumarchaeota                                 1.895141e-05
## Korarchaeota                                   1.880226e-05
## Placozoa                                       1.426135e-05
## Candidatus Poribacteria                        1.034724e-05
## Hemichordata                                   4.906748e-06
## Nanoarchaeota                                  1.627075e-06

The most prevalent phyla are Firmicutes (52.7% average abundance), Bacteroidetes (35.4%), Actinobacteria (4.7%), Proteobacteria (3.9%) and Fusobaceria (0.43%).

What is the standard deviation of phyla with the highest relative abundance?

RelAbund.Phyla.Filt.zerofilt[1:5, 1:10]
## # A tibble: 5 × 10
##   Sample_Name              Pig   Diet  Time_Point Diet_By_Time_Po… Acidobacteria
##   <chr>                    <fct> <fct> <fct>      <fct>                    <dbl>
## 1 ShotgunWGS-ControlPig6G… 6     Cont… Day 14     Control Day 14        0.000741
## 2 ShotgunWGS-ControlPig8G… 8     Cont… Day 0      Control Day 0         0.000885
## 3 ShotgunWGS-ControlPig3G… 3     Cont… Day 14     Control Day 14        0.000690
## 4 ShotgunWGS-TomatoPig14G… 14    Toma… Day 7      Tomato Day 7          0.000732
## 5 ShotgunWGS-ControlPig5G… 5     Cont… Day 7      Control Day 7         0.000656
## # … with 4 more variables: Actinobacteria <dbl>, Apicomplexa <dbl>,
## #   Aquificae <dbl>, Ascomycota <dbl>
phyla_sd <- RelAbund.Phyla.Filt.zerofilt %>%
  summarize_if(is.numeric, sd)

phyla_sd_t <- t(phyla_sd)
phyla_sd_t <- as.data.frame(phyla_sd_t)

phyla_sd_t <- phyla_sd_t %>%
  rename(sd_phyla = V1) %>%
  arrange(-sd_phyla)

head(phyla_sd_t)
##                   sd_phyla
## Bacteroidetes  0.059401369
## Firmicutes     0.055579086
## Actinobacteria 0.018163420
## Proteobacteria 0.012837716
## Euryarchaeota  0.001445649
## Cyanobacteria  0.001357954

The standard deviations of most prevalent phyla are Firmicutes (5.5% average abundance), Bacteroidetes (5.9%), Actinobacteria (1.8%), Proteobacteria (1.2%) and Fusobaceria (8.5 x 10^4%).

What percent of the reads are from Bacteria?

final_phyla_bacteriaonly <- final_phyla_withdomain %>%
  filter(domain == "Bacteria")

final_phyla_bacteriaonly <- final_phyla_bacteriaonly$phylum

# select columns corresponding to bacteria
RelAbund.Phyla.Filt.zerofilt.baconly <- RelAbund.Phyla.Filt.zerofilt %>%
  select(contains(final_phyla_bacteriaonly)) 

# create rowsums
RelAbund.Phyla.Filt.zerofilt.baconly <- RelAbund.Phyla.Filt.zerofilt.baconly %>%
  mutate(rowsums = rowSums(RelAbund.Phyla.Filt.zerofilt.baconly[])) 

mean(RelAbund.Phyla.Filt.zerofilt.baconly$rowsums)
## [1] 0.9930777
sd(RelAbund.Phyla.Filt.zerofilt.baconly$rowsums)
## [1] 0.002045929

PERMANOVA

All samples, full model

Repeated measures, using Pig as a block and set permutations using how() ORIGINAL BLOCK

set.seed(2021)
# create factors
factors_time_diet_pig_phyla <- RelAbund.Phyla.Filt.zerofilt %>% 
  select(Time_Point, Diet, Pig)

# create permutations
perm_time_diet_pig_phyla <- how(nperm = 9999)
setBlocks(perm_time_diet_pig_phyla) <- with(factors_time_diet_pig_phyla, Pig)

# run permanova
AllData.Phyla.Filt.permanova <- adonis2(RelAbund.Phyla.Filt.zerofilt[,-c(1:5)]~Diet*Time_Point,
                                        data = factors_time_diet_pig_phyla,
                                        permutations = perm_time_diet_pig_phyla,
                                        method = "bray")

AllData.Phyla.Filt.permanova
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Blocks:  with(factors_time_diet_pig_phyla, Pig) 
## Permutation: free
## Number of permutations: 9999
## 
## adonis2(formula = RelAbund.Phyla.Filt.zerofilt[, -c(1:5)] ~ Diet * Time_Point, data = factors_time_diet_pig_phyla, permutations = perm_time_diet_pig_phyla, method = "bray")
##                 Df SumOfSqs      R2      F Pr(>F)   
## Diet             1 0.007675 0.02656 1.6746 0.0136 * 
## Time_Point       2 0.028496 0.09860 3.1087 0.0046 **
## Diet:Time_Point  2 0.005338 0.01847 0.5824 0.4915   
## Residual        54 0.247497 0.85637                 
## Total           59 0.289007 1.00000                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
  • Diet: p = 0.0150, significant
  • Time_Point: p = 0.0054, significant
  • Diet*Time_Point: p = 0.4870, non-significant

Interaction:

# create factors
Pig <- as.factor(RelAbund.Phyla.Filt.zerofilt$Pig)
Diet <- as.factor(RelAbund.Phyla.Filt.zerofilt$Diet)


# create permutations
perm_time_diet_pig_phyla <- how(within = Within(type="series", constant=TRUE),
                                plots = Plots(strata=Pig, type="free",))
# run permanova
AllData.Phyla.Filt.permanova <- adonis2(RelAbund.Phyla.Filt.zerofilt[,-c(1:5)]~Diet*Time_Point,
                                        data = factors_time_diet_pig_phyla,
                                        permutations = perm_time_diet_pig_phyla,
                                        method = "bray",
                                        by = "margin")

AllData.Phyla.Filt.permanova
## Permutation test for adonis under reduced model
## Marginal effects of terms
## Plots: Pig, plot permutation: free
## Permutation: series constant permutation within each Plot
## Number of permutations: 199
## 
## adonis2(formula = RelAbund.Phyla.Filt.zerofilt[, -c(1:5)] ~ Diet * Time_Point, data = factors_time_diet_pig_phyla, permutations = perm_time_diet_pig_phyla, method = "bray", by = "margin")
##                 Df SumOfSqs      R2      F Pr(>F)
## Diet:Time_Point  2 0.005338 0.01847 0.5824  0.515
## Residual        54 0.247497 0.85637              
## Total           59 0.289007 1.00000

Interaction not significant (p=.51), so remove from model

# create factors
Pig <- as.factor(RelAbund.Phyla.Filt.zerofilt$Pig)
Diet <- as.factor(RelAbund.Phyla.Filt.zerofilt$Diet)


# create permutations
perm_time_diet_pig_phyla <- how(within = Within(type="series", constant=TRUE),
                                plots = Plots(strata=Pig, type = "free"))
# run permanova
AllData.Phyla.Filt.permanova <- adonis2(RelAbund.Phyla.Filt.zerofilt[,-c(1:5)]~Diet + Time_Point,
                                        data = factors_time_diet_pig_phyla,
                                        permutations = perm_time_diet_pig_phyla,
                                        method = "bray",
                                        by = "margin")

AllData.Phyla.Filt.permanova
## Permutation test for adonis under reduced model
## Marginal effects of terms
## Plots: Pig, plot permutation: free
## Permutation: series constant permutation within each Plot
## Number of permutations: 199
## 
## adonis2(formula = RelAbund.Phyla.Filt.zerofilt[, -c(1:5)] ~ Diet + Time_Point, data = factors_time_diet_pig_phyla, permutations = perm_time_diet_pig_phyla, method = "bray", by = "margin")
##            Df SumOfSqs      R2      F Pr(>F)  
## Diet        1 0.007675 0.02656 1.7000  0.295  
## Time_Point  2 0.028496 0.09860 3.1558  0.015 *
## Residual   56 0.252835 0.87484                
## Total      59 0.289007 1.00000                
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Test for homogeneity of multivariate dispersions

dis <- vegdist(RelAbund.Phyla.Filt.zerofilt[,-c(1:5)], method = "bray")
mod <- betadisper(dis, Diet)
permutest(mod)
## 
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 999
## 
## Response: Distances
##           Df   Sum Sq    Mean Sq     F N.Perm Pr(>F)
## Groups     1 0.000153 0.00015277 0.121    999  0.726
## Residuals 58 0.073213 0.00126230

Non significant! good for our PERMANOVA test validity

Post Hoc PERMANOVA within Time

Within Control Diet Only

Effect of control diet over time.

# filter data set for only control samples
control.RelAbund.Phyla.zerofilt <- subset(RelAbund.Phyla.Filt.zerofilt, Diet == "Control")

# create factors
factors_control_pig_phyla <- droplevels(control.RelAbund.Phyla.zerofilt %>% 
  select(Time_Point, Pig))

# create permutations
perm_control_pig_phyla <- how(within = Within(type="series", constant=TRUE),
                                plots = Plots(strata=factors_control_pig_phyla$Pig, type = "free"))

# run PERMANOVA
Control.ByTime.Phyla.zerofilt.permanova <- adonis2(control.RelAbund.Phyla.zerofilt[,-c(1:5)]~Time_Point,
        data = factors_control_pig_phyla,
        permutations = perm_control_pig_phyla, 
        method = "bray",
        by = "margin")

Control.ByTime.Phyla.zerofilt.permanova
## Permutation test for adonis under NA model
## Marginal effects of terms
## Plots: factors_control_pig_phyla$Pig, plot permutation: free
## Permutation: series constant permutation within each Plot
## Number of permutations: 199
## 
## adonis2(formula = control.RelAbund.Phyla.zerofilt[, -c(1:5)] ~ Time_Point, data = factors_control_pig_phyla, permutations = perm_control_pig_phyla, method = "bray", by = "margin")
##            Df SumOfSqs      R2      F Pr(>F)   
## Time_Point  2 0.025943 0.17486 2.8609   0.01 **
## Residual   27 0.122422 0.82514                 
## Total      29 0.148365 1.00000                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Significant effect of time (p = 0.005) within control samples. Beta diversity changing with time. Now the question is where is the difference coming from (ie. between which time points?)

Control T1 vs Control T2
# filter data set for only samples at T1 and T2
control.T1T2.RelAbund.Phyla.zerofilt <- subset(control.RelAbund.Phyla.zerofilt, Time_Point != "Day 14")

# create factors
factors_control_T1T2_pig_phyla <- droplevels(control.T1T2.RelAbund.Phyla.zerofilt %>% 
  select(Time_Point, Pig))

# create permutations
perm_control_T1T2_pig_phyla <- how(within = Within(type="series", constant=TRUE),
                                   plots = Plots(strata=factors_control_T1T2_pig_phyla$Pig,
                                                 type = "free"))

# run PERMANOVA
Control.T1T2.Phyla.zerofilt.permanova <- adonis2(control.T1T2.RelAbund.Phyla.zerofilt[,-c(1:5)]~Time_Point,
        data = factors_control_T1T2_pig_phyla,
        permutations = perm_control_T1T2_pig_phyla, 
        method = "bray",
        by = "margin")

Control.T1T2.Phyla.zerofilt.permanova
## Permutation test for adonis under NA model
## Marginal effects of terms
## Plots: factors_control_T1T2_pig_phyla$Pig, plot permutation: free
## Permutation: series constant permutation within each Plot
## Number of permutations: 199
## 
## adonis2(formula = control.T1T2.RelAbund.Phyla.zerofilt[, -c(1:5)] ~ Time_Point, data = factors_control_T1T2_pig_phyla, permutations = perm_control_T1T2_pig_phyla, method = "bray", by = "margin")
##            Df SumOfSqs      R2      F Pr(>F)  
## Time_Point  1  0.01404 0.11986 2.4513   0.03 *
## Residual   18  0.10309 0.88014                
## Total      19  0.11713 1.00000                
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

p=.085 so not significant between T1 and T2

Control T1 vs Control T3
# filter data set for only samples at T1 and T3
control.T1T3.RelAbund.Phyla.zerofilt <- subset(control.RelAbund.Phyla.zerofilt, Time_Point != "Day 7")

# create factors
factors_control_T1T3_pig_phyla <- droplevels(control.T1T3.RelAbund.Phyla.zerofilt %>% 
  select(Time_Point, Pig))

# create permutations
perm_control_T1T3_pig_phyla <- how(within = Within(type="series", constant=TRUE),
                                   plots = Plots(strata=factors_control_T1T3_pig_phyla$Pig,
                                                 type = "free"))

# run PERMANOVA
Control.T1T3.Phyla.zerofilt.permanova <- adonis2(control.T1T3.RelAbund.Phyla.zerofilt[,-c(1:5)]~Time_Point,
        data = factors_control_T1T3_pig_phyla,
        permutations = perm_control_T1T3_pig_phyla, 
        method = "bray",
        by = "margin")

Control.T1T3.Phyla.zerofilt.permanova
## Permutation test for adonis under NA model
## Marginal effects of terms
## Plots: factors_control_T1T3_pig_phyla$Pig, plot permutation: free
## Permutation: series constant permutation within each Plot
## Number of permutations: 199
## 
## adonis2(formula = control.T1T3.RelAbund.Phyla.zerofilt[, -c(1:5)] ~ Time_Point, data = factors_control_T1T3_pig_phyla, permutations = perm_control_T1T3_pig_phyla, method = "bray", by = "margin")
##            Df SumOfSqs      R2      F Pr(>F)  
## Time_Point  1 0.022462 0.28421 7.1469   0.02 *
## Residual   18 0.056572 0.71579                
## Total      19 0.079034 1.00000                
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

P = .02 so significant. There is a significant difference between T1 and T3 in the control diet pigs

Control T2 vs Control T3
# filter data set for only samples at T2 and T3
control.T2T3.RelAbund.Phyla.zerofilt <- subset(control.RelAbund.Phyla.zerofilt, Time_Point != "Day 0")

# create factors
factors_control_T2T3_pig_phyla <- droplevels(control.T2T3.RelAbund.Phyla.zerofilt %>% 
  select(Time_Point, Pig))

# create permutations
perm_control_T2T3_pig_phyla <- how(within = Within(type="series", constant=TRUE),
                                   plots = Plots(strata=factors_control_T2T3_pig_phyla$Pig,
                                                 type = "free"))

# run PERMANOVA
Control.T2T3.Phyla.zerofilt.permanova <- adonis2(control.T2T3.RelAbund.Phyla.zerofilt[,-c(1:5)]~Time_Point,
        data = factors_control_T2T3_pig_phyla,
        permutations = perm_control_T2T3_pig_phyla, 
        method = "bray",
        by = "margin")

Control.T2T3.Phyla.zerofilt.permanova
## Permutation test for adonis under NA model
## Marginal effects of terms
## Plots: factors_control_T2T3_pig_phyla$Pig, plot permutation: free
## Permutation: series constant permutation within each Plot
## Number of permutations: 199
## 
## adonis2(formula = control.T2T3.RelAbund.Phyla.zerofilt[, -c(1:5)] ~ Time_Point, data = factors_control_T2T3_pig_phyla, permutations = perm_control_T2T3_pig_phyla, method = "bray", by = "margin")
##            Df SumOfSqs      R2    F Pr(>F)
## Time_Point  1 0.002413 0.02755 0.51   0.33
## Residual   18 0.085178 0.97245            
## Total      19 0.087591 1.00000

P = .315 so not significant

Within Tomato Diet Only

Effect of tomato diet over time.

# filter data for only tomato samples
tomato.RelAbund.Phyla.zerofilt <- subset(RelAbund.Phyla.Filt.zerofilt, Diet == "Tomato")

# create factors
factors_tomato_pig_phyla <- tomato.RelAbund.Phyla.zerofilt %>% 
  select(Time_Point, Pig)

# create permutations
perm_tomato_pig_phyla <- how(within = Within(type="series", constant=TRUE),
                             plots = Plots(strata=factors_tomato_pig_phyla$Pig, type = "free"))

# run PERMANOVA
tomato.ByTime.Phyla.zerofilt.permanova <- adonis2(tomato.RelAbund.Phyla.zerofilt[,-c(1:5)]~Time_Point,
                                                  data = factors_tomato_pig_phyla,
                                                  permutations = perm_tomato_pig_phyla, 
                                                  method = "bray",
                                                  by = "margin")

tomato.ByTime.Phyla.zerofilt.permanova
## Permutation test for adonis under NA model
## Marginal effects of terms
## Plots: factors_tomato_pig_phyla$Pig, plot permutation: free
## Permutation: series constant permutation within each Plot
## Number of permutations: 199
## 
## adonis2(formula = tomato.RelAbund.Phyla.zerofilt[, -c(1:5)] ~ Time_Point, data = factors_tomato_pig_phyla, permutations = perm_tomato_pig_phyla, method = "bray", by = "margin")
##            Df SumOfSqs      R2      F Pr(>F)
## Time_Point  2 0.007891 0.05935 0.8517   0.34
## Residual   27 0.125075 0.94065              
## Total      29 0.132966 1.00000

Non-significant effect of time (p = 0.325) within tomato samples. So no post hoc tests necessary.

Subset by time

Day 0 Only

Effect of diet at day 0.

# filter data set for only day 0 samples
d0.RelAbund.Phyla.zerofilt <- subset(RelAbund.Phyla.Filt.zerofilt, Time_Point == "Day 0")

# create factors
# don't need to include pig, since no repeated measures here 
# only testing Diet within a time point
factors_day0_phyla <- d0.RelAbund.Phyla.zerofilt %>% 
  select(Diet)

# create permutations
perm_day0_phyla <- how(nperm = 9999)

# run PERMANOVA
d0.Phyla.zerofilt.permanova <- adonis2(d0.RelAbund.Phyla.zerofilt[,-c(1:5)]~Diet,
                                       data = factors_day0_phyla,
                                       permutations = perm_day0_phyla, 
                                       method = "bray")

d0.Phyla.zerofilt.permanova
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 9999
## 
## adonis2(formula = d0.RelAbund.Phyla.zerofilt[, -c(1:5)] ~ Diet, data = factors_day0_phyla, permutations = perm_day0_phyla, method = "bray")
##          Df SumOfSqs      R2     F Pr(>F)
## Diet      1 0.003515 0.04898 0.927 0.3692
## Residual 18 0.068249 0.95102             
## Total    19 0.071764 1.00000

Non-significant effect of diet (p=0.376) at day 0.

Day 7 Only

Effect of diet at day 7.

# filter data set for only day 7 samples
d7.RelAbund.Phyla.zerofilt <- subset(RelAbund.Phyla.Filt.zerofilt, Time_Point == "Day 7")

# create factors
# don't need to include pig, since no repeated measures here 
# only testing Diet within a time point
factors_day7_phyla <- d7.RelAbund.Phyla.zerofilt %>% 
  select(Diet)

# create permutations
perm_day7_phyla <- how(nperm = 9999)

# run PERMANOVA
d7.Phyla.zerofilt.permanova <- adonis2(d7.RelAbund.Phyla.zerofilt[,-c(1:5)]~Diet,
                                       data = factors_day7_phyla,
                                       permutations = perm_day7_phyla, 
                                       method = "bray")

d7.Phyla.zerofilt.permanova
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 9999
## 
## adonis2(formula = d7.RelAbund.Phyla.zerofilt[, -c(1:5)] ~ Diet, data = factors_day7_phyla, permutations = perm_day7_phyla, method = "bray")
##          Df SumOfSqs      R2      F Pr(>F)
## Diet      1 0.005267 0.04205 0.7901 0.4009
## Residual 18 0.119990 0.95795              
## Total    19 0.125257 1.00000

Non-significant effect of diet (p=0.4097) at day 7.

Day 14 Only

Effect of diet at day 14.

# filter data set for only day 14 samples
d14.RelAbund.Phyla.zerofilt <- subset(RelAbund.Phyla.Filt.zerofilt, Time_Point == "Day 14")

# create factors
# don't need to include pig, since no repeated measures here 
# only testing Diet within a time point
factors_day14_phyla <- d14.RelAbund.Phyla.zerofilt %>% 
  select(Diet)

# create permutations
perm_day14_phyla <- how(nperm = 9999)

# run PERMANOVA
d14.Phyla.zerofilt.permanova <- adonis2(d14.RelAbund.Phyla.zerofilt[,-c(1:5)]~Diet,
                                       data = factors_day14_phyla,
                                       permutations = perm_day14_phyla, 
                                       method = "bray")

d14.Phyla.zerofilt.permanova
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 9999
## 
## adonis2(formula = d14.RelAbund.Phyla.zerofilt[, -c(1:5)] ~ Diet, data = factors_day14_phyla, permutations = perm_day14_phyla, method = "bray")
##          Df SumOfSqs      R2      F Pr(>F)
## Diet      1 0.004232 0.06665 1.2854 0.2691
## Residual 18 0.059258 0.93335              
## Total    19 0.063490 1.00000

Non-significant effect of diet (p=0.256) at day 14.

PCoA Beta Diversity

All samples

# calculate distances
phyla.filt.dist.zeros <- vegdist(RelAbund.Phyla.Filt.zerofilt[6:ncol(RelAbund.Phyla.Filt.zerofilt)], 
                                 method = "bray")

# do multi-dimensional scaling (the PCoA calculations) on those distances
scale.phyla.filt.zerofilt <- cmdscale(phyla.filt.dist.zeros, k=2)

# make into data frame and bind metadata
scale.phyla.filt.zerofilt.df <- as.data.frame(cbind(scale.phyla.filt.zerofilt, AllSamples.Metadata))

# do PCoA again, but get eigen values
scale.phyla.filt.zerofilt.eig <- cmdscale(phyla.filt.dist.zeros, k=2, eig = TRUE)

# convert eigenvalues to percentages and assign to a variable
eigs.phyla.filt.zerofilt <- (100*((scale.phyla.filt.zerofilt.eig$eig)/(sum(scale.phyla.filt.zerofilt.eig$eig))))

# round the converted eigenvalues
round.eigs.phyla.filt.zerofilt <- round(eigs.phyla.filt.zerofilt, 3)

Plot

PCoA_phyla_20zeros_allsamples <- scale.phyla.filt.zerofilt.df %>%
ggplot(aes(x=`1`, y=`2`, fill = Diet_By_Time_Point)) +
  geom_point(size = 3, color = "black", shape = 21, alpha = 0.9) +
  scale_fill_manual(values=c("skyblue1", "dodgerblue", "royalblue4", "sienna1","firebrick3","tomato4")) +
  theme_classic() +
  theme(axis.text = element_text(color = "black"))+
  labs(x=paste("PC1: ", round.eigs.phyla.filt.zerofilt[1], "%"), 
       y=paste("PC2: ", round.eigs.phyla.filt.zerofilt[2], "%"), 
       fill="Diet & Time Point",
       title = "Beta Diversity",
       subtitle = "Phyla Level") 

PCoA_phyla_20zeros_allsamples

ggsave("Figures/BetaDiversity_PCoA_Phyla_allsamples.png", 
       plot = PCoA_phyla_20zeros_allsamples, 
       dpi = 800, 
       width = 10, 
       height = 8)

Facet by time point

Re-level factors

scale.phyla.filt.zerofilt.df <- scale.phyla.filt.zerofilt.df %>% 
  mutate(Time_Point = fct_relevel(Time_Point, c("Day 0", "Day 7", "Day 14")))
PCoA_phyla_20zeros_facetbytime <- scale.phyla.filt.zerofilt.df %>%
ggplot(aes(x=`1`, y=`2`, fill = Diet_By_Time_Point)) +
  geom_hline(yintercept = 0, color = "light grey", linetype = "dashed", size = 0.3) +
  geom_vline(xintercept = 0, color = "light grey", linetype = "dashed", size = 0.3) +
  geom_point(size = 3, color = "black", shape = 21, alpha = 0.9) +
  scale_fill_manual(values=c("skyblue1", "dodgerblue", "royalblue4", "sienna1","firebrick3","tomato4")) +
  theme_bw() +
  theme(axis.text = element_text(color = "black"),
        strip.background =element_rect(fill="white"),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  labs(x=paste("PC1: ", round.eigs.phyla.filt.zerofilt[1], "%"), 
       y=paste("PC2: ", round.eigs.phyla.filt.zerofilt[2], "%"), 
       fill="Diet & Time Point",
       title = "Beta Diversity",
       subtitle = "Phyla Level, Subset by Time Point") +
  facet_wrap(~Time_Point)

PCoA_phyla_20zeros_facetbytime

ggsave("Figures/BetaDiversity_PCoA_Phyla_FacetByTimePoint.png", 
       plot = PCoA_phyla_20zeros_facetbytime, 
       dpi = 800, 
       width = 10, 
       height = 6)

Facet by diet

PCoA_phyla_20zeros_facetbydiet <- scale.phyla.filt.zerofilt.df %>%
ggplot(aes(x=`1`, y=`2`, fill = Diet_By_Time_Point)) +
  geom_hline(yintercept = 0, color = "light grey", linetype = "dashed", size = 0.3) +
  geom_vline(xintercept = 0, color = "light grey", linetype = "dashed", size = 0.3) +
  geom_point(size = 3, color = "black", shape = 21, alpha = 0.9) +
  scale_fill_manual(values=c("skyblue1", "dodgerblue", "royalblue4", "sienna1","firebrick3","tomato4")) +
  theme_bw() +
  theme(axis.text = element_text(color = "black"),
        strip.background =element_rect(fill="white"),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  labs(x=paste("PC1: ", round.eigs.phyla.filt.zerofilt[1], "%"), 
       y=paste("PC2: ", round.eigs.phyla.filt.zerofilt[2], "%"), 
       fill="Diet & Time Point",
       title = "Beta Diversity",
       subtitle = "Phyla Level, Subset by Diet") +
  facet_wrap(~Diet)

PCoA_phyla_20zeros_facetbydiet

ggsave("Figures/BetaDiversity_PCoA_Phyla_FacetByDiet.png", 
       plot = PCoA_phyla_20zeros_facetbydiet, 
       dpi = 800, 
       width = 10, 
       height = 8)

Subset

Ended up not using this as part of the paper. Since the input is different here (i.e., the PCoA only has the subset data as an input) the output looks slightly different.

Control only

# calculate distances
control.phyla.filt.dist.zeros <- vegdist(control.RelAbund.Phyla.zerofilt[,-c(1:5)], method = "bray")

# do PCoA calculations
control.scale.phyla.filt.zerofilt <- cmdscale(control.phyla.filt.dist.zeros, k=2)

# filter meta data
meta.control <- subset(AllSamples.Metadata, Diet == "Control")

# make pcoa table into data frame and bind metadata to it
control.scale.phyla.filt.zerofilt.df <- as.data.frame(cbind(meta.control, control.scale.phyla.filt.zerofilt))

# do PCoA again, but get eigenvalues
control.scale.phyla.filt.zerofilt.eig <- cmdscale(control.phyla.filt.dist.zeros, k=2, eig = TRUE)

# convert eigenvalues to percentages and assign to a variable
control.eigs.phyla.filt.zerofilt <- (100*((control.scale.phyla.filt.zerofilt.eig$eig)/sum(control.scale.phyla.filt.zerofilt.eig$eig)))

# round the eigenvalues
round.control.eigs.phyla.filt.zerofilt <- round(control.eigs.phyla.filt.zerofilt, 3)

Re-level factors

control.scale.phyla.filt.zerofilt.df$Time_Point <- factor(control.scale.phyla.filt.zerofilt.df$Time_Point, 
                                                          levels = c("Day 0", "Day 7", "Day 14"))

Plot

control.scale.phyla.filt.zerofilt.df %>%
ggplot(aes(x=`1`, y=`2`, fill = Time_Point)) +
  geom_point(size=3, shape = 21, color = "black", alpha = 0.9) +
  scale_fill_manual(values=c("skyblue1", "dodgerblue", "royalblue4")) +
  theme_classic() +
  theme(axis.text = element_text(color = "black")) +
  labs(x=paste("PC1: ", round.control.eigs.phyla.filt.zerofilt[1], "%"), 
       y=paste("PC2: ", round.control.eigs.phyla.filt.zerofilt[2], "%"), 
       fill="Time Point",
       title = "Beta Diversity",
       subtitle = "Phyla Level, Control Only")

Tomato only

# calculate distances
tomato.phyla.filt.dist.zeros <- vegdist(tomato.RelAbund.Phyla.zerofilt[,-c(1:5)], method = "bray")

# do PCoA calculations
tomato.scale.phyla.filt.zerofilt <- cmdscale(tomato.phyla.filt.dist.zeros, k=2)

# filter meta data
meta.tomato <- subset(AllSamples.Metadata, Diet == "Tomato")

# make pcoa table into data frame and bind metadata to it
tomato.scale.phyla.filt.zerofilt.df <- as.data.frame(cbind(meta.tomato, tomato.scale.phyla.filt.zerofilt))

# do PCoA again, but get eigenvalues
tomato.scale.phyla.filt.zerofilt.eig <- cmdscale(tomato.phyla.filt.dist.zeros, k=2, eig = TRUE)

# convert eigenvalues to percentages and assign to a variable
tomato.eigs.phyla.filt.zerofilt <- (100*((tomato.scale.phyla.filt.zerofilt.eig$eig)/sum(tomato.scale.phyla.filt.zerofilt.eig$eig)))

# round the eigenvalues
round.tomato.eigs.phyla.filt.zerofilt <- round(tomato.eigs.phyla.filt.zerofilt, 3)

Re-level factors

tomato.scale.phyla.filt.zerofilt.df$Time_Point <- factor(tomato.scale.phyla.filt.zerofilt.df$Time_Point, 
                                                         levels = c("Day 0", "Day 7", "Day 14"))

Plot

tomato.scale.phyla.filt.zerofilt.df %>%
ggplot(aes(x=`1`, y=`2`, fill = Time_Point)) +
  geom_point(size=3, shape = 21, color = "black", alpha = 0.9) +
  scale_fill_manual(values=c("sienna1","firebrick3","tomato4")) +
  theme_classic() +
  theme(axis.text = element_text(color = "black")) +
  labs(x=paste("PC1: ", round.tomato.eigs.phyla.filt.zerofilt[1], "%"), 
       y=paste("PC2: ", round.tomato.eigs.phyla.filt.zerofilt[2], "%"), 
       fill="Time Point",
       title = "Beta Diversity",
       subtitle = "Phyla Level, Tomato Only")

Day 0 Only

# calculate distances
d0.phyla.filt.dist.zeros <- vegdist(d0.RelAbund.Phyla.zerofilt[,-c(1:5)], method = "bray")

# do PCoA calculations
d0.scale.phyla.filt.zerofilt <- cmdscale(d0.phyla.filt.dist.zeros, k=2)

# filter meta data
meta.d0 <- subset(AllSamples.Metadata, Time_Point == "Day 0")

# make pcoa table into data frame and bind metadata to it
d0.scale.phyla.filt.zerofilt.df <- as.data.frame(cbind(meta.d0, d0.scale.phyla.filt.zerofilt))

# do PCoA again, but get eigenvalues
d0.scale.phyla.filt.zerofilt.eig <- cmdscale(d0.phyla.filt.dist.zeros, k=2, eig = TRUE)

# convert eigenvalues to percentages and assign to a variable
d0.eigs.phyla.filt.zerofilt <- (100*((d0.scale.phyla.filt.zerofilt.eig$eig)/sum(d0.scale.phyla.filt.zerofilt.eig$eig)))

# round the eigenvalues
round.d0.eigs.phyla.filt.zerofilt <- round(d0.eigs.phyla.filt.zerofilt, 3)

Plot

d0.scale.phyla.filt.zerofilt.df %>%
  ggplot(aes(x = `1`, y = `2`, fill = Diet)) +
  geom_point(size=3, shape = 21, color = "black", alpha = 0.9) +
  scale_fill_manual(values=c("steelblue2", "tomato2")) +
  theme_classic() +
  theme(axis.text = element_text(color = "black")) +
  labs(x=paste("PC1: ", round.d0.eigs.phyla.filt.zerofilt[1], "%"), 
       y=paste("PC2: ", round.d0.eigs.phyla.filt.zerofilt[2], "%"), 
       fill="Time Point",
       title = "Beta Diversity",
       subtitle = "Phyla Level, Day 0 Only")

Day 7 Only

# calculate distances
d7.phyla.filt.dist.zeros <- vegdist(d7.RelAbund.Phyla.zerofilt[,-c(1:5)], method = "bray")

# do PCoA calculations
d7.scale.phyla.filt.zerofilt <- cmdscale(d7.phyla.filt.dist.zeros, k=2)

# filter meta data
meta.d7 <- subset(AllSamples.Metadata, Time_Point == "Day 7")

# make pcoa table into data frame and bind metadata to it
d7.scale.phyla.filt.zerofilt.df <- as.data.frame(cbind(meta.d7, d7.scale.phyla.filt.zerofilt))

# do PCoA again, but get eigenvalues
d7.scale.phyla.filt.zerofilt.eig <- cmdscale(d7.phyla.filt.dist.zeros, k=2, eig = TRUE)

# convert eigenvalues to percentages and assign to a variable
d7.eigs.phyla.filt.zerofilt <- (100*((d7.scale.phyla.filt.zerofilt.eig$eig)/sum(d7.scale.phyla.filt.zerofilt.eig$eig)))

# round the eigenvalues
round.d7.eigs.phyla.filt.zerofilt <- round(d7.eigs.phyla.filt.zerofilt, 3)

Plot

d7.scale.phyla.filt.zerofilt.df %>%
  ggplot(aes(x = `1`, y = `2`, fill = Diet)) +
  geom_point(size=3, shape = 21, color = "black", alpha = 0.9) +
  scale_fill_manual(values=c("steelblue2", "tomato2")) +
  theme_classic() +
  theme(axis.text = element_text(color = "black")) +
  labs(x=paste("PC1: ", round.d7.eigs.phyla.filt.zerofilt[1], "%"), 
       y=paste("PC2: ", round.d7.eigs.phyla.filt.zerofilt[2], "%"), 
       fill="Time Point",
       title = "Beta Diversity",
       subtitle = "Phyla Level, Day 7 Only")

Day 14 Only

# calculate distances
d14.phyla.filt.dist.zeros <- vegdist(d14.RelAbund.Phyla.zerofilt[,-c(1:5)], method = "bray")
# do PCoA calculations
d14.scale.phyla.filt.zerofilt <- cmdscale(d14.phyla.filt.dist.zeros, k=2)

# filter meta data
meta.d14 <- subset(AllSamples.Metadata, Time_Point == "Day 14")

# make pcoa table into data frame and bind metadata to it
d14.scale.phyla.filt.zerofilt.df <- as.data.frame(cbind(meta.d14, d14.scale.phyla.filt.zerofilt))

# do PCoA again, but get eigenvalues
d14.scale.phyla.filt.zerofilt.eig <- cmdscale(d14.phyla.filt.dist.zeros, k=2, eig = TRUE)

# convert eigenvalues to percentages and assign to a variable
d14.eigs.phyla.filt.zerofilt <- (100*((d14.scale.phyla.filt.zerofilt.eig$eig)/sum(d14.scale.phyla.filt.zerofilt.eig$eig)))

# round the eigenvalues
round.d14.eigs.phyla.filt.zerofilt <- round(d14.eigs.phyla.filt.zerofilt, 3)

Plot

d14.scale.phyla.filt.zerofilt.df %>%
  ggplot(aes(x = `1`, y = `2`, fill = Diet)) +
  geom_point(size=3, shape = 21, color = "black", alpha = 0.9) +
  scale_fill_manual(values=c("steelblue2", "tomato2")) +
  theme_classic() +
  theme(axis.text = element_text(color = "black")) +
  labs(x=paste("PC1: ", round.d14.eigs.phyla.filt.zerofilt[1], "%"), 
       y=paste("PC2: ", round.d14.eigs.phyla.filt.zerofilt[2], "%"), 
       fill="Time Point",
       title = "Beta Diversity",
       subtitle = "Phyla Level, Day 14 Only")

Bacteroidota/Bacteriodetes, Bacilotta/Firmicutes, and their ratio

Given a priori interest in the phyla Bacteroidota/Bacteriodetes and Bacilotta/Firmicutes, we are conducted repeated measures ANOVA analysis for their changes in our samples. The ratio of Bacteroidota to Bacilotta is a commonly used metric for assessing the health of the microbiome, with a higher Bacteroidota to Bacilotta (formerly B to F) ratio being more beneficial.

Wrangling

dim(RelAbund.Phyla.Filt.zerofilt)
## [1] 60 50

60 samples, and 45 phyla (5 columns are metadata).

Re-level Time_Point

RelAbund.Phyla.Filt.zerofilt <- RelAbund.Phyla.Filt.zerofilt %>%
  mutate(Time_Point = fct_relevel(Time_Point, c("Day 0", "Day 7", "Day 14")))

levels(RelAbund.Phyla.Filt.zerofilt$Time_Point)
## [1] "Day 0"  "Day 7"  "Day 14"

Add column Other_phyla with the sum of all phyla that are not Bacteroidetes or Firmicutes

RelAbund.Phyla.Filt.zerofilt.withother <- RelAbund.Phyla.Filt.zerofilt %>%
  mutate(Other_phyla = rowSums(select(.[6:ncol(.)], !contains(c("Bacteroidetes", "Firmicutes")))))

kable(head(RelAbund.Phyla.Filt.zerofilt.withother))
Sample_Name Pig Diet Time_Point Diet_By_Time_Point Acidobacteria Actinobacteria Apicomplexa Aquificae Ascomycota Bacillariophyta Bacteroidetes Basidiomycota Candidatus Poribacteria Chlamydiae Chlorobi Chloroflexi Chlorophyta Chrysiogenetes Crenarchaeota Cyanobacteria Deferribacteres Deinococcus-Thermus Dictyoglomi Elusimicrobia Euryarchaeota Fibrobacteres Firmicutes Fusobacteria Gemmatimonadetes Hemichordata Korarchaeota Lentisphaerae Microsporidia Nanoarchaeota Nematoda Nitrospirae Placozoa Planctomycetes Proteobacteria Spirochaetes Synergistetes Tenericutes Thaumarchaeota Thermotogae Verrucomicrobia unclassified (derived from Bacteria) unclassified (derived from Eukaryota) unclassified (derived from Viruses) unclassified (derived from other sequences) Other_phyla
ShotgunWGS-ControlPig6GutMicrobiome-Day14 6 Control Day 14 Control Day 14 0.0007406 0.0481336 0.0000595 0.0005033 0.0003842 2.71e-05 0.3670956 6.18e-05 6.70e-06 0.0001422 0.0012598 0.0020208 0.0000953 8.53e-05 0.0001670 0.0022775 0.0003850 0.0006393 0.0003136 0.0001629 0.0033951 0.0012287 0.5308271 0.0039555 0.0000544 6.7e-06 1.93e-05 0.0001971 1.26e-05 1.0e-06 0.0000459 0.0001420 1.75e-05 0.0003925 0.0271370 0.0029683 0.0011475 0.0007123 1.44e-05 0.0012921 0.0008269 0.0003085 0.0003247 0.0003984 1.24e-05 0.1020763
ShotgunWGS-ControlPig8GutMicrobiome-Day0 8 Control Day 0 Control Day 0 0.0008850 0.0659807 0.0000914 0.0005366 0.0005228 4.38e-05 0.3312767 9.64e-05 1.17e-05 0.0001974 0.0014459 0.0022885 0.0001359 9.90e-05 0.0002148 0.0032408 0.0004747 0.0008538 0.0003700 0.0002398 0.0045655 0.0016578 0.5293436 0.0050574 0.0000809 4.3e-06 2.31e-05 0.0004364 2.10e-05 1.2e-06 0.0000631 0.0001898 1.62e-05 0.0006266 0.0368314 0.0041577 0.0017830 0.0008883 1.90e-05 0.0016916 0.0014957 0.0004095 0.0009973 0.0006252 7.90e-06 0.1393790
ShotgunWGS-ControlPig3GutMicrobiome-Day14 3 Control Day 14 Control Day 14 0.0006897 0.0326727 0.0000492 0.0004253 0.0003318 3.34e-05 0.3263817 5.13e-05 5.40e-06 0.0001435 0.0011574 0.0019673 0.0000938 7.02e-05 0.0001652 0.0029204 0.0003675 0.0006011 0.0002872 0.0001810 0.0033125 0.0012911 0.5870258 0.0037180 0.0000624 4.1e-06 1.35e-05 0.0002005 1.45e-05 1.0e-06 0.0000378 0.0001458 8.00e-06 0.0004066 0.0271624 0.0028287 0.0010743 0.0006457 1.53e-05 0.0013084 0.0009681 0.0002973 0.0003279 0.0005188 1.61e-05 0.0865920
ShotgunWGS-TomatoPig14GutMicrobiome-Day7 14 Tomato Day 7 Tomato Day 7 0.0007324 0.0329202 0.0001398 0.0005384 0.0005593 5.41e-05 0.3461498 9.49e-05 1.41e-05 0.0001856 0.0013024 0.0023710 0.0001531 9.49e-05 0.0002730 0.0028512 0.0004519 0.0008048 0.0003529 0.0002988 0.0069066 0.0014564 0.5231174 0.0046147 0.0000508 4.2e-06 3.00e-05 0.0004095 2.00e-05 5.0e-06 0.0001148 0.0002006 1.58e-05 0.0004494 0.0597393 0.0046887 0.0015563 0.0010752 3.08e-05 0.0016628 0.0010478 0.0009662 0.0005509 0.0008405 9.57e-05 0.1307244
ShotgunWGS-ControlPig5GutMicrobiome-Day7 5 Control Day 7 Control Day 7 0.0006564 0.0463444 0.0000557 0.0004617 0.0003835 3.61e-05 0.2599966 5.93e-05 7.20e-06 0.0001644 0.0012486 0.0020345 0.0001263 8.89e-05 0.0002123 0.0034222 0.0003796 0.0006821 0.0002920 0.0002165 0.0043266 0.0013153 0.6250284 0.0043172 0.0000687 2.6e-06 2.15e-05 0.0004359 2.60e-05 1.3e-06 0.0000524 0.0001573 8.80e-06 0.0005450 0.0371768 0.0035022 0.0014189 0.0007518 1.79e-05 0.0014739 0.0011206 0.0004307 0.0004457 0.0004802 4.20e-06 0.1149734
ShotgunWGS-TomatoPig18GutMicrobiome-Day7 18 Tomato Day 7 Tomato Day 7 0.0007724 0.1016953 0.0000567 0.0003691 0.0004482 3.59e-05 0.3872593 6.68e-05 9.50e-06 0.0001245 0.0012329 0.0018259 0.0001234 5.10e-05 0.0001537 0.0041846 0.0002496 0.0006182 0.0002373 0.0001290 0.0027879 0.0011617 0.4453529 0.0028428 0.0001167 1.1e-06 1.40e-05 0.0001616 1.80e-05 6.0e-07 0.0000752 0.0001374 1.63e-05 0.0006148 0.0389241 0.0026521 0.0008313 0.0005133 1.46e-05 0.0009592 0.0013244 0.0008027 0.0002906 0.0007107 3.31e-05 0.1673878

Add column B to F

RelAbund.Phyla.Filt.zerofilt.withother.BtoF <- RelAbund.Phyla.Filt.zerofilt.withother %>%
  mutate(BtoF = Bacteroidetes/Firmicutes)

kable(head(RelAbund.Phyla.Filt.zerofilt.withother.BtoF))
Sample_Name Pig Diet Time_Point Diet_By_Time_Point Acidobacteria Actinobacteria Apicomplexa Aquificae Ascomycota Bacillariophyta Bacteroidetes Basidiomycota Candidatus Poribacteria Chlamydiae Chlorobi Chloroflexi Chlorophyta Chrysiogenetes Crenarchaeota Cyanobacteria Deferribacteres Deinococcus-Thermus Dictyoglomi Elusimicrobia Euryarchaeota Fibrobacteres Firmicutes Fusobacteria Gemmatimonadetes Hemichordata Korarchaeota Lentisphaerae Microsporidia Nanoarchaeota Nematoda Nitrospirae Placozoa Planctomycetes Proteobacteria Spirochaetes Synergistetes Tenericutes Thaumarchaeota Thermotogae Verrucomicrobia unclassified (derived from Bacteria) unclassified (derived from Eukaryota) unclassified (derived from Viruses) unclassified (derived from other sequences) Other_phyla BtoF
ShotgunWGS-ControlPig6GutMicrobiome-Day14 6 Control Day 14 Control Day 14 0.0007406 0.0481336 0.0000595 0.0005033 0.0003842 2.71e-05 0.3670956 6.18e-05 6.70e-06 0.0001422 0.0012598 0.0020208 0.0000953 8.53e-05 0.0001670 0.0022775 0.0003850 0.0006393 0.0003136 0.0001629 0.0033951 0.0012287 0.5308271 0.0039555 0.0000544 6.7e-06 1.93e-05 0.0001971 1.26e-05 1.0e-06 0.0000459 0.0001420 1.75e-05 0.0003925 0.0271370 0.0029683 0.0011475 0.0007123 1.44e-05 0.0012921 0.0008269 0.0003085 0.0003247 0.0003984 1.24e-05 0.1020763 0.6915539
ShotgunWGS-ControlPig8GutMicrobiome-Day0 8 Control Day 0 Control Day 0 0.0008850 0.0659807 0.0000914 0.0005366 0.0005228 4.38e-05 0.3312767 9.64e-05 1.17e-05 0.0001974 0.0014459 0.0022885 0.0001359 9.90e-05 0.0002148 0.0032408 0.0004747 0.0008538 0.0003700 0.0002398 0.0045655 0.0016578 0.5293436 0.0050574 0.0000809 4.3e-06 2.31e-05 0.0004364 2.10e-05 1.2e-06 0.0000631 0.0001898 1.62e-05 0.0006266 0.0368314 0.0041577 0.0017830 0.0008883 1.90e-05 0.0016916 0.0014957 0.0004095 0.0009973 0.0006252 7.90e-06 0.1393790 0.6258254
ShotgunWGS-ControlPig3GutMicrobiome-Day14 3 Control Day 14 Control Day 14 0.0006897 0.0326727 0.0000492 0.0004253 0.0003318 3.34e-05 0.3263817 5.13e-05 5.40e-06 0.0001435 0.0011574 0.0019673 0.0000938 7.02e-05 0.0001652 0.0029204 0.0003675 0.0006011 0.0002872 0.0001810 0.0033125 0.0012911 0.5870258 0.0037180 0.0000624 4.1e-06 1.35e-05 0.0002005 1.45e-05 1.0e-06 0.0000378 0.0001458 8.00e-06 0.0004066 0.0271624 0.0028287 0.0010743 0.0006457 1.53e-05 0.0013084 0.0009681 0.0002973 0.0003279 0.0005188 1.61e-05 0.0865920 0.5559920
ShotgunWGS-TomatoPig14GutMicrobiome-Day7 14 Tomato Day 7 Tomato Day 7 0.0007324 0.0329202 0.0001398 0.0005384 0.0005593 5.41e-05 0.3461498 9.49e-05 1.41e-05 0.0001856 0.0013024 0.0023710 0.0001531 9.49e-05 0.0002730 0.0028512 0.0004519 0.0008048 0.0003529 0.0002988 0.0069066 0.0014564 0.5231174 0.0046147 0.0000508 4.2e-06 3.00e-05 0.0004095 2.00e-05 5.0e-06 0.0001148 0.0002006 1.58e-05 0.0004494 0.0597393 0.0046887 0.0015563 0.0010752 3.08e-05 0.0016628 0.0010478 0.0009662 0.0005509 0.0008405 9.57e-05 0.1307244 0.6617057
ShotgunWGS-ControlPig5GutMicrobiome-Day7 5 Control Day 7 Control Day 7 0.0006564 0.0463444 0.0000557 0.0004617 0.0003835 3.61e-05 0.2599966 5.93e-05 7.20e-06 0.0001644 0.0012486 0.0020345 0.0001263 8.89e-05 0.0002123 0.0034222 0.0003796 0.0006821 0.0002920 0.0002165 0.0043266 0.0013153 0.6250284 0.0043172 0.0000687 2.6e-06 2.15e-05 0.0004359 2.60e-05 1.3e-06 0.0000524 0.0001573 8.80e-06 0.0005450 0.0371768 0.0035022 0.0014189 0.0007518 1.79e-05 0.0014739 0.0011206 0.0004307 0.0004457 0.0004802 4.20e-06 0.1149734 0.4159757
ShotgunWGS-TomatoPig18GutMicrobiome-Day7 18 Tomato Day 7 Tomato Day 7 0.0007724 0.1016953 0.0000567 0.0003691 0.0004482 3.59e-05 0.3872593 6.68e-05 9.50e-06 0.0001245 0.0012329 0.0018259 0.0001234 5.10e-05 0.0001537 0.0041846 0.0002496 0.0006182 0.0002373 0.0001290 0.0027879 0.0011617 0.4453529 0.0028428 0.0001167 1.1e-06 1.40e-05 0.0001616 1.80e-05 6.0e-07 0.0000752 0.0001374 1.63e-05 0.0006148 0.0389241 0.0026521 0.0008313 0.0005133 1.46e-05 0.0009592 0.0013244 0.0008027 0.0002906 0.0007107 3.31e-05 0.1673878 0.8695561

Convert data from wide to long (i.e. make data tidy)

RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long <- RelAbund.Phyla.Filt.zerofilt.withother.BtoF %>%
  pivot_longer(cols = 6:ncol(.),
               names_to = "phylum",
               values_to = "rel_abund")

RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long[1:10,]
## # A tibble: 10 × 7
##    Sample_Name          Pig   Diet  Time_Point Diet_By_Time_Po… phylum rel_abund
##    <chr>                <fct> <fct> <fct>      <fct>            <chr>      <dbl>
##  1 ShotgunWGS-ControlP… 6     Cont… Day 14     Control Day 14   Acido…   7.41e-4
##  2 ShotgunWGS-ControlP… 6     Cont… Day 14     Control Day 14   Actin…   4.81e-2
##  3 ShotgunWGS-ControlP… 6     Cont… Day 14     Control Day 14   Apico…   5.95e-5
##  4 ShotgunWGS-ControlP… 6     Cont… Day 14     Control Day 14   Aquif…   5.03e-4
##  5 ShotgunWGS-ControlP… 6     Cont… Day 14     Control Day 14   Ascom…   3.84e-4
##  6 ShotgunWGS-ControlP… 6     Cont… Day 14     Control Day 14   Bacil…   2.71e-5
##  7 ShotgunWGS-ControlP… 6     Cont… Day 14     Control Day 14   Bacte…   3.67e-1
##  8 ShotgunWGS-ControlP… 6     Cont… Day 14     Control Day 14   Basid…   6.18e-5
##  9 ShotgunWGS-ControlP… 6     Cont… Day 14     Control Day 14   Candi…   6.70e-6
## 10 ShotgunWGS-ControlP… 6     Cont… Day 14     Control Day 14   Chlam…   1.42e-4

Plotting

Stacked bar chart of B, F, and all the other phyla

RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long.BFandOther <-
  RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long %>%
    filter(phylum %in% c("Bacteroidetes", "Firmicutes", "Other_phyla"))

head(RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long.BFandOther)
## # A tibble: 6 × 7
##   Sample_Name           Pig   Diet  Time_Point Diet_By_Time_Po… phylum rel_abund
##   <chr>                 <fct> <fct> <fct>      <fct>            <chr>      <dbl>
## 1 ShotgunWGS-ControlPi… 6     Cont… Day 14     Control Day 14   Bacte…     0.367
## 2 ShotgunWGS-ControlPi… 6     Cont… Day 14     Control Day 14   Firmi…     0.531
## 3 ShotgunWGS-ControlPi… 6     Cont… Day 14     Control Day 14   Other…     0.102
## 4 ShotgunWGS-ControlPi… 8     Cont… Day 0      Control Day 0    Bacte…     0.331
## 5 ShotgunWGS-ControlPi… 8     Cont… Day 0      Control Day 0    Firmi…     0.529
## 6 ShotgunWGS-ControlPi… 8     Cont… Day 0      Control Day 0    Other…     0.139
RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long.BFandOther %>%
  ggplot(aes(x=as.numeric(Pig), y=rel_abund, fill=phylum))+
  geom_col()+
  scale_fill_brewer(palette = "GnBu") +
  facet_grid(~Time_Point)+
  theme_classic()+
  labs(y="Relative Abundance", 
       fill="Phylum",
       x = "Pig") +
  theme(panel.grid = element_blank(), axis.text = element_text(color="black"),
        strip.text = element_text(color = "black", size = 14), 
        strip.background = element_blank())

B to F Ratio

Plotting

B to F boxplot with jitter

RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long.BtoF <- 
  RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long %>%
  filter(phylum == "BtoF")

head(RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long.BtoF)
## # A tibble: 6 × 7
##   Sample_Name           Pig   Diet  Time_Point Diet_By_Time_Po… phylum rel_abund
##   <chr>                 <fct> <fct> <fct>      <fct>            <chr>      <dbl>
## 1 ShotgunWGS-ControlPi… 6     Cont… Day 14     Control Day 14   BtoF       0.692
## 2 ShotgunWGS-ControlPi… 8     Cont… Day 0      Control Day 0    BtoF       0.626
## 3 ShotgunWGS-ControlPi… 3     Cont… Day 14     Control Day 14   BtoF       0.556
## 4 ShotgunWGS-TomatoPig… 14    Toma… Day 7      Tomato Day 7     BtoF       0.662
## 5 ShotgunWGS-ControlPi… 5     Cont… Day 7      Control Day 7    BtoF       0.416
## 6 ShotgunWGS-TomatoPig… 18    Toma… Day 7      Tomato Day 7     BtoF       0.870
BtoF_Boxplot <- RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long.BtoF %>%
  ggplot(aes(x=Diet, y=rel_abund, fill=Diet_By_Time_Point))+
  geom_boxplot(outlier.shape = NA)+
  geom_point(aes(fill = Diet_By_Time_Point), color = "black", alpha = 0.7, position=position_jitterdodge()) +
  scale_fill_manual(values = c("skyblue1", "dodgerblue", "royalblue4", 
                               "sienna1","firebrick3","tomato4")) +
  scale_color_manual(values = c("skyblue1", "dodgerblue", "royalblue4", 
                               "sienna1","firebrick3","tomato4")) +
  ylim(0, 1) +
  theme_minimal() +
  theme(axis.text.x = element_text(size = 12, color = "black"), 
        axis.text.y = element_text(color = "black"), 
        panel.grid.minor = element_blank()) +
  labs(x=NULL, 
       y= "Bacteroidota to Bacillota", 
       fill="Diet & Time Point",
       title = "Ratio of Bacteroidota to Bacillota") 

BtoF_Boxplot
## Warning: Removed 5 rows containing non-finite values (stat_boxplot).
## Warning: Removed 5 rows containing missing values (geom_point).

Saving

ggsave("Figures/BacteroidotatoBacilottaRatio_Boxplot.png", 
       plot = BtoF_Boxplot, 
       dpi = 800, 
       width = 7, 
       height = 5)

Statistics

head(RelAbund.Phyla.Filt.zerofilt.withother.BtoF)
## # A tibble: 6 × 52
##   Sample_Name              Pig   Diet  Time_Point Diet_By_Time_Po… Acidobacteria
##   <chr>                    <fct> <fct> <fct>      <fct>                    <dbl>
## 1 ShotgunWGS-ControlPig6G… 6     Cont… Day 14     Control Day 14        0.000741
## 2 ShotgunWGS-ControlPig8G… 8     Cont… Day 0      Control Day 0         0.000885
## 3 ShotgunWGS-ControlPig3G… 3     Cont… Day 14     Control Day 14        0.000690
## 4 ShotgunWGS-TomatoPig14G… 14    Toma… Day 7      Tomato Day 7          0.000732
## 5 ShotgunWGS-ControlPig5G… 5     Cont… Day 7      Control Day 7         0.000656
## 6 ShotgunWGS-TomatoPig18G… 18    Toma… Day 7      Tomato Day 7          0.000772
## # … with 46 more variables: Actinobacteria <dbl>, Apicomplexa <dbl>,
## #   Aquificae <dbl>, Ascomycota <dbl>, Bacillariophyta <dbl>,
## #   Bacteroidetes <dbl>, Basidiomycota <dbl>, `Candidatus Poribacteria` <dbl>,
## #   Chlamydiae <dbl>, Chlorobi <dbl>, Chloroflexi <dbl>, Chlorophyta <dbl>,
## #   Chrysiogenetes <dbl>, Crenarchaeota <dbl>, Cyanobacteria <dbl>,
## #   Deferribacteres <dbl>, `Deinococcus-Thermus` <dbl>, Dictyoglomi <dbl>,
## #   Elusimicrobia <dbl>, Euryarchaeota <dbl>, Fibrobacteres <dbl>, …
# select only columns used for ANOVA
RelAbund.Phyla.Filt.zerofilt.withother.BtoF.ForANOVA <- RelAbund.Phyla.Filt.zerofilt.withother.BtoF %>%
  select(Pig, Diet, Time_Point, BtoF)

Repeated measures ANOVA of B to F ratio

BtoF.Ratio.ANOVA <- anova_test(data = RelAbund.Phyla.Filt.zerofilt.withother.BtoF.ForANOVA, 
                          formula = BtoF ~ Diet*Time_Point + Error(Pig/(Time_Point)),
                          dv = BtoF, wid = Pig, between = Diet, within = Time_Point)

get_anova_table(BtoF.Ratio.ANOVA)
## ANOVA Table (type II tests)
## 
##            Effect DFn DFd     F     p p<.05   ges
## 1            Diet   1  18 0.125 0.728       0.004
## 2      Time_Point   2  36 5.437 0.009     * 0.113
## 3 Diet:Time_Point   2  36 0.850 0.436       0.020
  • Significant effect of time point (p = 0.009)
  • Nonsignificant effect of diet (p = 0.728)
  • Nonsignificant effect of diet:timepoint (p = 0.436)

Use posthoc to see where is significant using a fdr p-value adjustment for multiple testing, grouping by time (both diets)

BtoF.Ratio.ANOVA.posthoc <- pairwise_t_test(BtoF ~ Time_Point, 
                                 data = RelAbund.Phyla.Filt.zerofilt.withother.BtoF, 
                                 paired = TRUE, 
                                 p.adjust.method = "fdr") 

BtoF.Ratio.ANOVA.posthoc
## # A tibble: 3 × 10
##   .y.   group1 group2    n1    n2 statistic    df     p p.adj p.adj.signif
## * <chr> <chr>  <chr>  <int> <int>     <dbl> <dbl> <dbl> <dbl> <chr>       
## 1 BtoF  Day 0  Day 7     20    20      1.42    19 0.172 0.235 ns          
## 2 BtoF  Day 0  Day 14    20    20      3.15    19 0.005 0.016 *           
## 3 BtoF  Day 7  Day 14    20    20      1.23    19 0.235 0.235 ns

Significant difference is between day 0 and day 14 (padj = 0.016).

Use posthoc to see where is significant using a fdr p-value adjustment for multiple testing, separating by diet

BtoF.Ratio.ANOVA.posthoc.bytime <- RelAbund.Phyla.Filt.zerofilt.withother.BtoF.ForANOVA %>%
  group_by(Diet) %>%
  pairwise_t_test(BtoF ~ Time_Point, 
                  paired = TRUE, 
                  p.adjust.method = "fdr")

BtoF.Ratio.ANOVA.posthoc.bytime
## # A tibble: 6 × 11
##   Diet  .y.   group1 group2    n1    n2 statistic    df     p p.adj p.adj.signif
## * <fct> <chr> <chr>  <chr>  <int> <int>     <dbl> <dbl> <dbl> <dbl> <chr>       
## 1 Cont… BtoF  Day 0  Day 7     10    10     1.34      9 0.213 0.32  ns          
## 2 Cont… BtoF  Day 0  Day 14    10    10     3.19      9 0.011 0.033 *           
## 3 Cont… BtoF  Day 7  Day 14    10    10     0.589     9 0.571 0.571 ns          
## 4 Toma… BtoF  Day 0  Day 7     10    10     0.403     9 0.696 0.696 ns          
## 5 Toma… BtoF  Day 0  Day 14    10    10     1.35      9 0.211 0.633 ns          
## 6 Toma… BtoF  Day 7  Day 14    10    10     0.812     9 0.438 0.657 ns

Significant in control between day 0 and 14, padj = 0.033. All else non-significant.

Bacteroidetes and Firmicutes

Plotting

Boxplotting

RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long.OnlyBandF <- 
  RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long %>%
    filter(phylum == "Bacteroidetes" | phylum == "Firmicutes")

btof.labs <- c("Bacteroidota", "Bacillota")
names(btof.labs) <- c("Bacteroidetes", "Firmicutes")

BandF_Boxplot <- RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long.OnlyBandF %>%
  ggplot(aes(x=Diet, y=rel_abund, fill=Diet_By_Time_Point))+
  geom_boxplot(outlier.shape = NA)+
  geom_point(aes(fill = Diet_By_Time_Point), color = "black", position=position_jitterdodge(), alpha = 0.7) +
  scale_fill_manual(values=c("skyblue1", "dodgerblue", "royalblue4", "sienna1","firebrick3","tomato4"))+
  ylim(0, 0.75) +
  theme_bw() +
  facet_wrap(~phylum, labeller = labeller(phylum = btof.labs))+
  labs(x=NULL, y= "Relative Abundance", fill="Diet & Time Point") +
  theme(axis.text.x = element_text(size = 11, color = "black"), 
        axis.text.y = element_text(color = "black"), 
        panel.grid.minor = element_blank(), 
        strip.text.x = element_text(color = "black", size = 14),
        strip.background = element_rect(fill = "white"))

BandF_Boxplot

Saving

ggsave("Figures/BacteroidotaBacilotta_Boxplot.png", 
       plot = BandF_Boxplot, 
       dpi = 800, 
       width = 7, 
       height = 5)

Statistics

Bacteroidetes
# select only columns used for ANOVA
RelAbund.Phyla.Filt.zerofilt.withother.BtoF.Bonly <- RelAbund.Phyla.Filt.zerofilt.withother.BtoF %>%
  select(Pig, Diet, Time_Point, Bacteroidetes)

Bacteroidetes repeated measures ANOVA

Bacteroidetes.ANOVA <- anova_test(data = RelAbund.Phyla.Filt.zerofilt.withother.BtoF.Bonly, 
                          formula = Bacteroidetes ~ Diet*Time_Point + Error(Pig/(Time_Point)),
                          dv = Bacteroidetes, wid = Pig, between = Diet, within = Time_Point)

get_anova_table(Bacteroidetes.ANOVA)
## ANOVA Table (type II tests)
## 
##            Effect DFn DFd     F     p p<.05      ges
## 1            Diet   1  18 0.009 0.928       0.000272
## 2      Time_Point   2  36 4.131 0.024     * 0.089000
## 3 Diet:Time_Point   2  36 0.700 0.503       0.016000
  • Significant effect of time point (p = 0.024)
  • Nonsignificant effect of diet (p = 0.928)
  • onsignificant effect of diet:timepoint (p = 0.503)

Use posthoc to see where is significant using a fdr p-value adjustment for multiple testing, grouping by time (both diets)

Bacteroidetes.ANOVA.posthoc <- pairwise_t_test(Bacteroidetes ~ Time_Point, 
                                 data = RelAbund.Phyla.Filt.zerofilt.withother.BtoF, 
                                 paired = TRUE, 
                                 p.adjust.method = "fdr") 

Bacteroidetes.ANOVA.posthoc
## # A tibble: 3 × 10
##   .y.         group1 group2    n1    n2 statistic    df     p p.adj p.adj.signif
## * <chr>       <chr>  <chr>  <int> <int>     <dbl> <dbl> <dbl> <dbl> <chr>       
## 1 Bacteroide… Day 0  Day 7     20    20     1.62     19 0.122 0.183 ns          
## 2 Bacteroide… Day 0  Day 14    20    20     2.56     19 0.019 0.058 ns          
## 3 Bacteroide… Day 7  Day 14    20    20     0.572    19 0.574 0.574 ns

Borderline significant difference is between day 0 and day 14 (padj = 0.058).

Use posthoc to see where is significant using a fdr p-value adjustment for multiple testing, separating by diet

Bacteroidetes.ANOVA.posthoc.bytime <- RelAbund.Phyla.Filt.zerofilt.withother.BtoF.Bonly %>%
  group_by(Diet) %>%
  pairwise_t_test(Bacteroidetes ~ Time_Point, 
                  paired = TRUE, 
                  p.adjust.method = "fdr")

Bacteroidetes.ANOVA.posthoc.bytime
## # A tibble: 6 × 11
##   Diet  .y.   group1 group2    n1    n2 statistic    df     p p.adj p.adj.signif
## * <fct> <chr> <chr>  <chr>  <int> <int>     <dbl> <dbl> <dbl> <dbl> <chr>       
## 1 Cont… Bact… Day 0  Day 7     10    10     1.56      9 0.154 0.231 ns          
## 2 Cont… Bact… Day 0  Day 14    10    10     3.02      9 0.015 0.044 *           
## 3 Cont… Bact… Day 7  Day 14    10    10     0.152     9 0.883 0.883 ns          
## 4 Toma… Bact… Day 0  Day 7     10    10     0.486     9 0.638 0.638 ns          
## 5 Toma… Bact… Day 0  Day 14    10    10     1.08      9 0.308 0.638 ns          
## 6 Toma… Bact… Day 7  Day 14    10    10     0.495     9 0.633 0.638 ns

Significant difference in control between day 0 and 14, padj = 0.044. All else non-significant.

Firmicutes
# select only columns used for ANOVA
RelAbund.Phyla.Filt.zerofilt.withother.BtoF.Fonly <- RelAbund.Phyla.Filt.zerofilt.withother.BtoF %>%
  select(Pig, Diet, Time_Point, Firmicutes)

Firmicutes repeated measures ANOVA

Firmicutes.ANOVA <- anova_test(data = RelAbund.Phyla.Filt.zerofilt.withother.BtoF.Fonly, 
                          formula = Firmicutes ~ Diet*Time_Point + Error(Pig/(Time_Point)),
                          dv = Firmicutes, wid = Pig, between = Diet, within = Time_Point)

get_anova_table(Firmicutes.ANOVA)
## ANOVA Table (type II tests)
## 
##            Effect DFn DFd     F     p p<.05   ges
## 1            Diet   1  18 1.079 0.313       0.033
## 2      Time_Point   2  36 8.102 0.001     * 0.161
## 3 Diet:Time_Point   2  36 0.993 0.380       0.023
  • Significant effect of time point (p = 0.001)
  • Nonsignificant effect of diet (p = 0.313)
  • Nonsignificant effect of diet:timepoint (p = 0.380)

Use posthoc to see where is significant using a fdr p-value adjustment for multiple testing, grouping by time (both diets)

Firmicutes.ANOVA.posthoc <- pairwise_t_test(Firmicutes ~ Time_Point, 
                                 data = RelAbund.Phyla.Filt.zerofilt.withother.BtoF, 
                                 paired = TRUE, 
                                 p.adjust.method = "fdr") 

Firmicutes.ANOVA.posthoc
## # A tibble: 3 × 10
##   .y.       group1 group2    n1    n2 statistic    df       p p.adj p.adj.signif
## * <chr>     <chr>  <chr>  <int> <int>     <dbl> <dbl>   <dbl> <dbl> <chr>       
## 1 Firmicut… Day 0  Day 7     20    20     -1.60    19 1.25e-1 0.125 ns          
## 2 Firmicut… Day 0  Day 14    20    20     -3.93    19 9.07e-4 0.003 **          
## 3 Firmicut… Day 7  Day 14    20    20     -1.67    19 1.11e-1 0.125 ns

Significant difference is between day 0 and day 14 (padj = 0.003).

Use posthoc to see where is significant using a fdr p-value adjustment for multiple testing, separating by diet

Firmicutes.ANOVA.posthoc.bytime <- RelAbund.Phyla.Filt.zerofilt.withother.BtoF.Fonly %>%
  group_by(Diet) %>%
  pairwise_t_test(Firmicutes ~ Time_Point, 
                  paired = TRUE, 
                  p.adjust.method = "fdr")

Firmicutes.ANOVA.posthoc.bytime
## # A tibble: 6 × 11
##   Diet  .y.   group1 group2    n1    n2 statistic    df     p p.adj p.adj.signif
## * <fct> <chr> <chr>  <chr>  <int> <int>     <dbl> <dbl> <dbl> <dbl> <chr>       
## 1 Cont… Firm… Day 0  Day 7     10    10    -1.48      9 0.173 0.259 ns          
## 2 Cont… Firm… Day 0  Day 14    10    10    -3.25      9 0.01  0.03  *           
## 3 Cont… Firm… Day 7  Day 14    10    10    -0.843     9 0.421 0.421 ns          
## 4 Toma… Firm… Day 0  Day 7     10    10    -0.550     9 0.596 0.596 ns          
## 5 Toma… Firm… Day 0  Day 14    10    10    -1.96      9 0.082 0.246 ns          
## 6 Toma… Firm… Day 7  Day 14    10    10    -1.05      9 0.322 0.483 ns

Significant difference is in control between day 0 and 14, padj = 0.030. All else non-significant.

Alpha Diversity

Calculated alpha diversity of phyla based on relative abundance, including all the filtering for implausible phyla and removing samples with more than 33.33% missing samples

Wrangling

dim(RelAbund.Phyla.Filt.zerofilt)
## [1] 60 50
RelAbund.Phyla.Filt.zerofilt[1:5,1:10]
## # A tibble: 5 × 10
##   Sample_Name              Pig   Diet  Time_Point Diet_By_Time_Po… Acidobacteria
##   <chr>                    <fct> <fct> <fct>      <fct>                    <dbl>
## 1 ShotgunWGS-ControlPig6G… 6     Cont… Day 14     Control Day 14        0.000741
## 2 ShotgunWGS-ControlPig8G… 8     Cont… Day 0      Control Day 0         0.000885
## 3 ShotgunWGS-ControlPig3G… 3     Cont… Day 14     Control Day 14        0.000690
## 4 ShotgunWGS-TomatoPig14G… 14    Toma… Day 7      Tomato Day 7          0.000732
## 5 ShotgunWGS-ControlPig5G… 5     Cont… Day 7      Control Day 7         0.000656
## # … with 4 more variables: Actinobacteria <dbl>, Apicomplexa <dbl>,
## #   Aquificae <dbl>, Ascomycota <dbl>

Wrangle

# move Sample_Name to rownames, remove metadata
RelAbund.Phyla.Filt.zerofilt.alphadiv <- RelAbund.Phyla.Filt.zerofilt

rownames(RelAbund.Phyla.Filt.zerofilt.alphadiv) <- RelAbund.Phyla.Filt.zerofilt.alphadiv$Sample_Name  
## Warning: Setting row names on a tibble is deprecated.
# remove metadata
RelAbund.Phyla.Filt.zerofilt.alphadiv <- RelAbund.Phyla.Filt.zerofilt.alphadiv %>%
  select(Acidobacteria:ncol(.))

RelAbund.Phyla.Filt.zerofilt.alphadiv[1:5,1:5]
## # A tibble: 5 × 5
##   Acidobacteria Actinobacteria Apicomplexa Aquificae Ascomycota
##           <dbl>          <dbl>       <dbl>     <dbl>      <dbl>
## 1      0.000741         0.0481   0.0000595  0.000503   0.000384
## 2      0.000885         0.0660   0.0000914  0.000537   0.000523
## 3      0.000690         0.0327   0.0000492  0.000425   0.000332
## 4      0.000732         0.0329   0.000140   0.000538   0.000559
## 5      0.000656         0.0463   0.0000557  0.000462   0.000384

Calculate alpha diversity

# run alpha diversity on phyla
phyla.filt.div <- diversity(RelAbund.Phyla.Filt.zerofilt.alphadiv, index = "shannon")

# convert to df
phyla.filt.div.df <- as.data.frame(phyla.filt.div)

# make column name 'shannon.phyla.filt'
colnames(phyla.filt.div.df) <- "shannon.phyla.filt"

head(phyla.filt.div.df)
##   shannon.phyla.filt
## 1           1.122832
## 2           1.231877
## 3           1.062241
## 4           1.221226
## 5           1.108308
## 6           1.261677

Combine with metadata

# combine with metadata
phyla.filt.div.df.meta <- cbind(RelAbund.Phyla.Filt.zerofilt[,1:5], phyla.filt.div.df)

head(phyla.filt.div.df.meta)
##                                 Sample_Name Pig    Diet Time_Point
## 1 ShotgunWGS-ControlPig6GutMicrobiome-Day14   6 Control     Day 14
## 2  ShotgunWGS-ControlPig8GutMicrobiome-Day0   8 Control      Day 0
## 3 ShotgunWGS-ControlPig3GutMicrobiome-Day14   3 Control     Day 14
## 4  ShotgunWGS-TomatoPig14GutMicrobiome-Day7  14  Tomato      Day 7
## 5  ShotgunWGS-ControlPig5GutMicrobiome-Day7   5 Control      Day 7
## 6  ShotgunWGS-TomatoPig18GutMicrobiome-Day7  18  Tomato      Day 7
##   Diet_By_Time_Point shannon.phyla.filt
## 1     Control Day 14           1.122832
## 2      Control Day 0           1.231877
## 3     Control Day 14           1.062241
## 4       Tomato Day 7           1.221226
## 5      Control Day 7           1.108308
## 6       Tomato Day 7           1.261677

Plotting

X-axis by diet

alpha.diversity.phyla.bydiet <- phyla.filt.div.df.meta %>%
  ggplot(aes(x = Diet, y = shannon.phyla.filt, fill = Diet_By_Time_Point)) +
  geom_boxplot(outlier.shape = NA) +
  geom_point(aes(fill = Diet_By_Time_Point), 
             color = "black", 
             alpha = 0.7, 
             position=position_jitterdodge()) +
  scale_fill_manual(values = c("skyblue1", "dodgerblue", "royalblue4", 
                               "sienna1","firebrick3","tomato4")) +
  theme_minimal() +
  theme(axis.text.x = element_text(size = 12, color = "black")) +
  labs(x=NULL, 
       y="Shannon diversity index", 
       title = "Alpha Diversity",
       subtitle = "Shannon Index, Phyla Level", 
       fill="Diet & Time Point")

alpha.diversity.phyla.bydiet

ggsave("Figures/AlphaDiversityPhyla_ByDiet_Boxplot.png", 
       plot = alpha.diversity.phyla.bydiet, 
       dpi = 800, 
       width = 10, 
       height = 6)

X-axis by time point

alpha.diversity.phyla.bytime <- phyla.filt.div.df.meta %>%
  ggplot(aes(x = Time_Point, y = shannon.phyla.filt, fill = Diet_By_Time_Point)) +
  geom_boxplot(outlier.shape = NA) +
  geom_point(color = "black", alpha = 0.7, position=position_jitterdodge()) +
  scale_fill_manual(values = c("skyblue1", "dodgerblue", "royalblue4", 
                               "sienna1","firebrick3","tomato4")) +
  theme_minimal() +
  theme(axis.text.x = element_text(size = 12, color = "black")) +
  labs(x=NULL, 
       y="Shannon diversity index", 
       title = "Alpha Diversity",
       subtitle = "Shannon Index, Phyla Level", 
       fill="Diet & Time Point")

alpha.diversity.phyla.bytime

ggsave("Figures/AlphaDiversityPhyla_ByTime_Boxplot.png", 
       plot = alpha.diversity.phyla.bytime, 
       dpi = 800, 
       width = 7, 
       height = 5)

Statistics

Repeated measures ANOVA

# must remove columns that aren't used in anova
head(phyla.filt.div.df.meta) 
##                                 Sample_Name Pig    Diet Time_Point
## 1 ShotgunWGS-ControlPig6GutMicrobiome-Day14   6 Control     Day 14
## 2  ShotgunWGS-ControlPig8GutMicrobiome-Day0   8 Control      Day 0
## 3 ShotgunWGS-ControlPig3GutMicrobiome-Day14   3 Control     Day 14
## 4  ShotgunWGS-TomatoPig14GutMicrobiome-Day7  14  Tomato      Day 7
## 5  ShotgunWGS-ControlPig5GutMicrobiome-Day7   5 Control      Day 7
## 6  ShotgunWGS-TomatoPig18GutMicrobiome-Day7  18  Tomato      Day 7
##   Diet_By_Time_Point shannon.phyla.filt
## 1     Control Day 14           1.122832
## 2      Control Day 0           1.231877
## 3     Control Day 14           1.062241
## 4       Tomato Day 7           1.221226
## 5      Control Day 7           1.108308
## 6       Tomato Day 7           1.261677
phyla.filt.div.foranova <- phyla.filt.div.df.meta[,-c(1,5)]

head(phyla.filt.div.foranova)
##   Pig    Diet Time_Point shannon.phyla.filt
## 1   6 Control     Day 14           1.122832
## 2   8 Control      Day 0           1.231877
## 3   3 Control     Day 14           1.062241
## 4  14  Tomato      Day 7           1.221226
## 5   5 Control      Day 7           1.108308
## 6  18  Tomato      Day 7           1.261677
phyla.filt.alphadiv.anova <- 
  anova_test(data = phyla.filt.div.foranova,
             formula = shannon.phyla.filt ~ Diet*Time_Point + Error(Pig/Time_Point),
             dv = shannon.phyla.filt, 
             wid = Pig, 
             within = Time_Point, 
             between = Diet)

get_anova_table(phyla.filt.alphadiv.anova)
## ANOVA Table (type II tests)
## 
##            Effect DFn DFd      F     p p<.05   ges
## 1            Diet   1  18 10.767 0.004     * 0.158
## 2      Time_Point   2  36  2.628 0.086       0.091
## 3 Diet:Time_Point   2  36  0.236 0.791       0.009
  • Significant effect of diet (p = 0.004)
  • Non-significant effect of timepoint (0.086)
  • Non-significant interaction of diet:time point (p = 0.791).

Check for normality

shapiro.test(phyla.filt.div.df.meta$shannon.phyla.filt)
## 
##  Shapiro-Wilk normality test
## 
## data:  phyla.filt.div.df.meta$shannon.phyla.filt
## W = 0.99022, p-value = 0.9135

Normal.

Post-hoc tests

posthoc.morevariables <- phyla.filt.div.foranova %>%
  group_by(Time_Point) %>%
  anova_test(dv = shannon.phyla.filt, wid = Pig, between = Diet) %>%
  get_anova_table() %>%
  adjust_pvalue(method = "fdr")
## Coefficient covariances computed by hccm()
## Coefficient covariances computed by hccm()
## Coefficient covariances computed by hccm()
posthoc.morevariables
## # A tibble: 3 × 9
##   Time_Point Effect   DFn   DFd     F     p `p<.05`   ges p.adj
##   <fct>      <chr>  <dbl> <dbl> <dbl> <dbl> <chr>   <dbl> <dbl>
## 1 Day 0      Diet       1    18  1.22 0.284 ""      0.063 0.284
## 2 Day 7      Diet       1    18  3.65 0.072 ""      0.169 0.108
## 3 Day 14     Diet       1    18  8.74 0.008 "*"     0.327 0.024

Significant effect of diet at day 14 (padj = 0.024)

posthoc.evenmorespecific <- phyla.filt.div.foranova %>%
  group_by(Time_Point) %>%
  pairwise_t_test(shannon.phyla.filt ~ Diet,
                  paired = TRUE,
                  p.adjust.method = "fdr")

posthoc.evenmorespecific
## # A tibble: 3 × 11
##   Time_Point .y.           group1 group2    n1    n2 statistic    df     p p.adj
## * <fct>      <chr>         <chr>  <chr>  <int> <int>     <dbl> <dbl> <dbl> <dbl>
## 1 Day 0      shannon.phyl… Contr… Tomato    10    10     -1.03     9 0.328 0.328
## 2 Day 7      shannon.phyl… Contr… Tomato    10    10     -1.93     9 0.085 0.085
## 3 Day 14     shannon.phyl… Contr… Tomato    10    10     -3.21     9 0.011 0.011
## # … with 1 more variable: p.adj.signif <chr>
  • Significant effect between control and tomato at day 14 (padj = 0.01)
  • Nonsignificant at day 0 (padj = 0.328)
  • Nonsignificant but getting close at day 7 (padj = 0.085)

ALDEx2

Quick introduction to anatomy of the aldex function

The aldex function does every step - data transformation and statistics
variable.name <- aldex(reads.data, variables.vector, mc.samples=#, test=“t”/“kw”, effect=T/F)
reads.data - your reads/count data, un changed
variables.vector - a vector of the variables corresponding to sample groups, in SAME order as sample names (and therefore columns)
mc.samples - here you tell the function how many Monte Carlo sampels to use with an integer (128 is typical)
test - which test do you want, t-test and wilcoxon, or anova-like and kruskal wallace? (will always do the parametric and non-parametric) t = t-test and wilcoxon kw = anova-like and kruskal wallace
effect - do you want it to incude effect results in output?

Key to aldex outputs - taken directly from vignette

  • we.ep - Expected P value of Welch’s t test
  • we.eBH - Expected Benjamini-Hochberg corrected P value of Welch’s t test
  • wi.ep - Expected P value of Wilcoxon rank test
  • wi.eBH - Expected Benjamini-Hochberg corrected P value of Wilcoxon test
  • kw.ep - Expected P value of Kruskal-Wallace test
  • kw.eBH - Expected Benjamini-Hochberg corrected P value of Kruskal-Wallace test
  • glm.ep - Expected P value of glm test
  • glm.eBH - Expected Benjamini-Hochberg corrected P value of glm test
  • rab.all - median clr value for all samples in the feature
  • rab.win.NS - median clr value for the NS group of samples
  • rab.win.S - median clr value for the S group of samples
  • dif.btw - median difference in clr values between S and NS groups
  • dif.win - median of the largest difference in clr values within S and NS groups
  • effect - median effect size: diff.btw / max(diff.win) for all instances
  • overlap - proportion of effect size that overlaps 0 (i.e. no effect)

ALDEx2 takes counts, not relative abundance.

We are using Benjamini Hochberg corrected pvalues, or we.eBH for t-tests (i.e., subsetting by time), and Benjamini-Hochberg corrected pvalues of the glm test glm.eBH for ANOVA tests (i.e., subsetting by diet)

Downloading ALDEx2

if (!requireNamespace("BiocManager", quietly = TRUE))
  install.packages("BiocManager")

BiocManager::install("ALDEx2")

Wrangling

Since we use counts for ALDEx2, we need to filter our counts data to include only the phyla we ended up using in our final analysis

# this data set filtered to remove inplausible phyla, but still includes phyla with a lot of missing values 
Phyla.Counts.Filt[1:10,1:10]
## # A tibble: 10 × 10
##    domain    phylum           `ShotgunWGS-Co…` `ShotgunWGS-Co…` `ShotgunWGS-Co…`
##    <chr>     <chr>                       <dbl>            <dbl>            <dbl>
##  1 Bacteria  Acidobacteria                2874             3717             2663
##  2 Bacteria  Actinobacteria             186789           277130           126155
##  3 Eukaryota Apicomplexa                   231              384              190
##  4 Bacteria  Aquificae                    1953             2254             1642
##  5 Eukaryota Ascomycota                   1491             2196             1281
##  6 Eukaryota Bacillariophyta               105              184              129
##  7 Bacteria  Bacteroidetes             1424565          1391417          1260217
##  8 Eukaryota Basidiomycota                 240              405              198
##  9 Eukaryota Blastocladiomyc…                0                0                0
## 10 Bacteria  Candidatus Pori…               26               49               21
## # … with 5 more variables: `ShotgunWGS-TomatoPig14GutMicrobiome-Day7` <dbl>,
## #   `ShotgunWGS-ControlPig5GutMicrobiome-Day7` <dbl>,
## #   `ShotgunWGS-TomatoPig18GutMicrobiome-Day7` <dbl>,
## #   `ShotgunWGS-TomatoPig16GutMicrobiome-Day7` <dbl>,
## #   `ShotgunWGS-ControlPig10GutMicrobiome-Day7` <dbl>
dim(Phyla.Counts.Filt)
## [1] 53 62
# final phyla list, after filtering for number of zeros
final_phyla[1:10,]
##  [1] "Acidobacteria"           "Actinobacteria"         
##  [3] "Apicomplexa"             "Aquificae"              
##  [5] "Ascomycota"              "Bacillariophyta"        
##  [7] "Bacteroidetes"           "Basidiomycota"          
##  [9] "Candidatus Poribacteria" "Chlamydiae"
# how many final phyla do we have?
dim(final_phyla)
## [1] 45  1
# join to create a df with phyla in rows, samples in columns
# filtered for genera used in this analysis
phyla_counts_foraldex <- inner_join(final_phyla, Phyla.Counts.Filt,
                                    by = "phylum")

dim(phyla_counts_foraldex)
## [1] 45 62
phyla_counts_foraldex[1:10, 1:4]
##                     phylum    domain ShotgunWGS-ControlPig6GutMicrobiome-Day14
## 1            Acidobacteria  Bacteria                                      2874
## 2           Actinobacteria  Bacteria                                    186789
## 3              Apicomplexa Eukaryota                                       231
## 4                Aquificae  Bacteria                                      1953
## 5               Ascomycota Eukaryota                                      1491
## 6          Bacillariophyta Eukaryota                                       105
## 7            Bacteroidetes  Bacteria                                   1424565
## 8            Basidiomycota Eukaryota                                       240
## 9  Candidatus Poribacteria  Bacteria                                        26
## 10              Chlamydiae  Bacteria                                       552
##    ShotgunWGS-ControlPig8GutMicrobiome-Day0
## 1                                      3717
## 2                                    277130
## 3                                       384
## 4                                      2254
## 5                                      2196
## 6                                       184
## 7                                   1391417
## 8                                       405
## 9                                        49
## 10                                      829
# add phyla as rownames
rownames(phyla_counts_foraldex) <- phyla_counts_foraldex$phylum

# remove phylum, domain as columns for cleaner data
phyla_counts_foraldex <- phyla_counts_foraldex %>%
  select(-phylum, -domain)

Subset by Time

Day 0

# subset day 0 only
Day0.Counts.Phyla.filt <- phyla_counts_foraldex %>% 
  select(ends_with("Day0"))

ALDEx2 function needs a factor of variables

# order alphabetically so making the meta data vector is easier
Day0.Counts.Phyla.filt <- Day0.Counts.Phyla.filt[order(colnames(Day0.Counts.Phyla.filt))]

Diets.Day0.Phyla <- as.vector(c(rep("Control", times=10), rep("Tomato", times=10)))

# check and make sure it came out right
Diets.Day0.Phyla
##  [1] "Control" "Control" "Control" "Control" "Control" "Control" "Control"
##  [8] "Control" "Control" "Control" "Tomato"  "Tomato"  "Tomato"  "Tomato" 
## [15] "Tomato"  "Tomato"  "Tomato"  "Tomato"  "Tomato"  "Tomato"

Run t-tests

# runs very slowly
# set cache = TRUE to save results
filt.Phyla.Day0.ByDiet.aldex <- aldex(Day0.Counts.Phyla.filt, 
                                       Diets.Day0.Phyla, 
                                       mc.samples = 1000, 
                                       test = "t", 
                                       effect = TRUE)
## aldex.clr: generating Monte-Carlo instances and clr values
## operating in serial mode
## computing center with all features
## aldex.ttest: doing t-test
## aldex.effect: calculating effect sizes
filt.Phyla.Day0.ByDiet.aldex <- 
  filt.Phyla.Day0.ByDiet.aldex[order(filt.Phyla.Day0.ByDiet.aldex$we.eBH, 
                                      decreasing = FALSE),]

kable(head(filt.Phyla.Day0.ByDiet.aldex))
rab.all rab.win.Control rab.win.Tomato diff.btw diff.win effect overlap we.ep we.eBH wi.ep wi.eBH
Proteobacteria 6.5857669 6.3145651 6.7528732 0.3746102 0.4414428 0.8688289 0.1646000 0.0083533 0.3221071 0.0110988 0.4064564
unclassified (derived from Eukaryota) 0.4397911 0.9033182 0.3721023 -0.4816460 0.3775456 -0.8855126 0.2372000 0.0259926 0.4121663 0.0520324 0.5629625
Chlorophyta -1.5919906 -1.6628591 -1.5087876 0.1732418 0.3127644 0.5175202 0.2666000 0.0670792 0.5280984 0.1077618 0.6142581
Tenericutes 0.9391923 1.0432714 0.8885737 -0.1583287 0.2037393 -0.6764046 0.2609478 0.0687257 0.5476915 0.0862333 0.6132091
Basidiomycota -2.2166312 -2.1306207 -2.3137243 -0.1761152 0.2950344 -0.5693332 0.2640000 0.0993361 0.5700791 0.1116548 0.6113181
Elusimicrobia -0.7859985 -0.8511297 -0.7338424 0.1259770 0.2378991 0.4818211 0.2789442 0.0942131 0.5839636 0.1284662 0.6345670

No significantly different phyla

hist(filt.Phyla.Day0.ByDiet.aldex$we.eBH,
     breaks = 45,
     main = "Histogram of p-values on the effect of diet at day 0 on phyla",
     xlab = "Benjamini Hochberg corrected p-value (we.eBH)")

we.eBH is the Benjamini-Hochberg corrected p-value, and nothing is < 0.05

Day 7

# subset day 7 only
Day7.Counts.Phyla.filt <- phyla_counts_foraldex %>% 
  select(ends_with("Day7"))

ALDEx2 function needs a factor of variables

# order alphabetically so making the meta data vector is easier
Day7.Counts.Phyla.filt <- Day7.Counts.Phyla.filt[order(colnames(Day7.Counts.Phyla.filt))]

Diets.Day7.Phyla <- as.vector(c(rep("Control", times=10), rep("Tomato", times=10)))

# check and make sure it came out right
Diets.Day7.Phyla
##  [1] "Control" "Control" "Control" "Control" "Control" "Control" "Control"
##  [8] "Control" "Control" "Control" "Tomato"  "Tomato"  "Tomato"  "Tomato" 
## [15] "Tomato"  "Tomato"  "Tomato"  "Tomato"  "Tomato"  "Tomato"

Run t-tests

# runs very slowly
# set cache = TRUE to save results
filt.Phyla.Day7.ByDiet.aldex <- aldex(Day7.Counts.Phyla.filt, 
                                       Diets.Day7.Phyla, 
                                       mc.samples = 1000, 
                                       test = "t", 
                                       effect = TRUE)
## aldex.clr: generating Monte-Carlo instances and clr values
## operating in serial mode
## computing center with all features
## aldex.ttest: doing t-test
## aldex.effect: calculating effect sizes
filt.Phyla.Day7.ByDiet.aldex <- 
  filt.Phyla.Day7.ByDiet.aldex[order(filt.Phyla.Day7.ByDiet.aldex$we.eBH, 
                                      decreasing = FALSE),]

kable(head(filt.Phyla.Day7.ByDiet.aldex))
rab.all rab.win.Control rab.win.Tomato diff.btw diff.win effect overlap we.ep we.eBH wi.ep wi.eBH
unclassified (derived from Bacteria) 0.6375097 0.3089784 1.0304263 0.6599291 0.3822769 1.5816905 0.0495901 0.0000614 0.0027560 0.0002156 0.0095993
Chrysiogenetes -2.2763611 -2.1569750 -2.4322655 -0.2743199 0.3633857 -0.7063056 0.2112000 0.0499035 0.3550747 0.0541899 0.3126401
Ascomycota 0.2293522 0.1351592 0.3842469 0.2343789 0.4023438 0.5322208 0.2358000 0.0434242 0.4152844 0.0628626 0.3649693
Firmicutes 10.4275493 10.6466196 10.2738925 -0.3318916 0.4093850 -0.6862980 0.2097580 0.0410232 0.4212510 0.0286925 0.2980693
Basidiomycota -2.1959373 -2.3467395 -2.0850216 0.2574169 0.4261325 0.5450211 0.2453509 0.0808754 0.4670333 0.0853502 0.3993034
Bacillariophyta -3.3771542 -3.5139314 -3.2717009 0.2555352 0.4569006 0.5304439 0.2777445 0.1266018 0.5031681 0.1621232 0.4897163

One phyla was significantly different by diet at day 7 - unclassified (derived from bacteria), padj = 0.002

hist(filt.Phyla.Day7.ByDiet.aldex$we.eBH,
     breaks = 45,
     main = "Histogram of p-values on the effect of diet at day 7 on phyla",
     xlab = "Benjamini Hochberg corrected p-value (we.eBH)")

Day 14

# subset day 14 only
Day14.Counts.Phyla.filt <- phyla_counts_foraldex %>% 
  select(ends_with("Day14"))

ALDEx2 function needs a factor of variables

# order alphabetically so making the meta data vector is easier
Day14.Counts.Phyla.filt <- Day14.Counts.Phyla.filt[order(colnames(Day14.Counts.Phyla.filt))]

Diets.Day14.Phyla <- as.vector(c(rep("Control", times=10), rep("Tomato", times=10)))

# check and make sure it came out right
Diets.Day14.Phyla
##  [1] "Control" "Control" "Control" "Control" "Control" "Control" "Control"
##  [8] "Control" "Control" "Control" "Tomato"  "Tomato"  "Tomato"  "Tomato" 
## [15] "Tomato"  "Tomato"  "Tomato"  "Tomato"  "Tomato"  "Tomato"

Run t-tests

filt.Phyla.Day14.ByDiet.aldex <- aldex(Day14.Counts.Phyla.filt, 
                                       Diets.Day14.Phyla, 
                                       mc.samples = 1000, 
                                       test = "t", 
                                       effect = TRUE)
## aldex.clr: generating Monte-Carlo instances and clr values
## operating in serial mode
## computing center with all features
## aldex.ttest: doing t-test
## aldex.effect: calculating effect sizes
filt.Phyla.Day14.ByDiet.aldex <- 
  filt.Phyla.Day14.ByDiet.aldex[order(filt.Phyla.Day14.ByDiet.aldex$we.eBH, 
                                      decreasing = FALSE),]

kable(head(filt.Phyla.Day14.ByDiet.aldex))
rab.all rab.win.Control rab.win.Tomato diff.btw diff.win effect overlap we.ep we.eBH wi.ep wi.eBH
unclassified (derived from Bacteria) 0.3878007 0.0656786 0.9924115 0.9148040 0.3457392 2.5428325 0.0000140 0.0000029 0.0001302 0.0000108 0.0004467
Nematoda -2.6749909 -2.9590197 -2.2600299 0.7407293 0.5273953 1.3785241 0.0388000 0.0006183 0.0083108 0.0003181 0.0043752
Apicomplexa -2.0607631 -2.4278818 -1.5566779 0.8009668 0.6160238 1.2318439 0.0901820 0.0011385 0.0131840 0.0015406 0.0147930
Deinococcus-Thermus 0.8934331 0.9663785 0.7934825 -0.1756483 0.1458435 -1.1470314 0.0980000 0.0062779 0.0330378 0.0047251 0.0286456
Proteobacteria 6.4735169 6.4218100 6.6020117 0.1992688 0.2071126 0.9684222 0.1323735 0.0064963 0.0402455 0.0063370 0.0366220
Firmicutes 10.6381375 10.6954982 10.4067027 -0.3137346 0.3339467 -0.8710526 0.1691662 0.0102429 0.0586362 0.0130723 0.0637137
hist(filt.Phyla.Day14.ByDiet.aldex$we.eBH,
     breaks = 45,
     main = "Histogram of p-values on the effect of diet at day 14 on phyla",
     xlab = "Benjamini Hochberg corrected p-value (we.eBH)")

How many significant phyla are there?

filt.Phyla.Day14.ByDiet.aldex.sig <- 
  filt.Phyla.Day14.ByDiet.aldex[which(filt.Phyla.Day14.ByDiet.aldex$we.eBH<0.05),]

length(rownames(filt.Phyla.Day14.ByDiet.aldex.sig))
## [1] 5

5 sig phyla

Which phyla are they?

sig_day14_phyla_aldex2 <- as.data.frame(cbind(rownames(filt.Phyla.Day14.ByDiet.aldex.sig),
                                 filt.Phyla.Day14.ByDiet.aldex.sig$we.eBH))

sig_day14_phyla_aldex2
##                                     V1                   V2
## 1 unclassified (derived from Bacteria) 0.000130184199239088
## 2                             Nematoda  0.00831079482853596
## 3                          Apicomplexa   0.0131839873014319
## 4                  Deinococcus-Thermus   0.0330378200022153
## 5                       Proteobacteria   0.0402455424599716
  • unclassified (derived from Bacteria)
  • Nematoda
  • Apicomplexa
  • Deinococcus-Thermus
  • Proteobacteria

What is the directionality of the change?

filt.Phyla.Day14.ByDiet.aldex %>%
  select(rab.win.Control, rab.win.Tomato, we.eBH) %>%
  filter(we.eBH <= 0.05)
##                                      rab.win.Control rab.win.Tomato
## unclassified (derived from Bacteria)      0.06567863      0.9924115
## Nematoda                                 -2.95901972     -2.2600299
## Apicomplexa                              -2.42788185     -1.5566779
## Deinococcus-Thermus                       0.96637845      0.7934825
## Proteobacteria                            6.42180997      6.6020117
##                                            we.eBH
## unclassified (derived from Bacteria) 0.0001301842
## Nematoda                             0.0083107948
## Apicomplexa                          0.0131839873
## Deinococcus-Thermus                  0.0330378200
## Proteobacteria                       0.0402455425

Higher in control:
* Deinococcus-Thermus

Higher in tomato:
* unclassified (derived from Bacteria)
* Nematoda
* Apicomplexa
* Proteobacteria

Subset by diet

Control

# subset control only samples across all time points, should be n=30
Control.Counts.Phyla.filt <- phyla_counts_foraldex %>% 
  select(contains("Control"))

ALDEx2 function needs a factor of variables

# results in pigs at different time points being grouped together
Control.Counts.Phyla.filt <- Control.Counts.Phyla.filt[order(colnames(Control.Counts.Phyla.filt))]

# then time point by "alphabetical" where 14 comes before 7
# ex, first few are Pig 10 Day 0, Pig 10 Day 14, Pig 10 Day 7, Pig 1 Day 0, Pig 1 Day 14, etc

TimePoints.Control.Phyla <- as.vector(rep(c("Day0", "Day14", "Day7"), times=10))

# check and make sure it looks right
TimePoints.Control.Phyla
##  [1] "Day0"  "Day14" "Day7"  "Day0"  "Day14" "Day7"  "Day0"  "Day14" "Day7" 
## [10] "Day0"  "Day14" "Day7"  "Day0"  "Day14" "Day7"  "Day0"  "Day14" "Day7" 
## [19] "Day0"  "Day14" "Day7"  "Day0"  "Day14" "Day7"  "Day0"  "Day14" "Day7" 
## [28] "Day0"  "Day14" "Day7"

More than two conditions this time, use the ANOVA-like test

filt.Phyla.Control.ByTime.aldex <- aldex(Control.Counts.Phyla.filt, 
                                          TimePoints.Control.Phyla, 
                                          mc.samples = 1000, 
                                          test = "kw", 
                                          effect = FALSE)
## aldex.clr: generating Monte-Carlo instances and clr values
## operating in serial mode
## computing center with all features
## aldex.glm: doing Kruskal-Wallace and glm test (ANOVA-like)
## operating in serial mode

We are looking at glm.eBH for the BH corrected ANOVA pval

filt.Phyla.Control.ByTime.aldex <- 
  filt.Phyla.Control.ByTime.aldex[order(filt.Phyla.Control.ByTime.aldex$glm.eBH, 
                                         decreasing = FALSE),]

kable(head(filt.Phyla.Control.ByTime.aldex))
kw.ep kw.eBH glm.ep glm.eBH
unclassified (derived from Bacteria) 0.0106850 0.1837993 0.0026968 0.0769864
unclassified (derived from Eukaryota) 0.0505786 0.2504227 0.0079943 0.1208772
Dictyoglomi 0.0266105 0.2116903 0.0224764 0.1449505
Verrucomicrobia 0.0385119 0.2325773 0.0257789 0.1537795
Firmicutes 0.0340934 0.2268098 0.0239987 0.1538562
Fibrobacteres 0.2588618 0.5236352 0.0316510 0.1592722
hist(filt.Phyla.Control.ByTime.aldex$glm.eBH,
     breaks = 45,
     main = "Histogram of p-values on the effect of time on control pigs on phyla",
     xlab = "Benjamini Hochberg corrected p-value (glm.eBH)")

How many significant phyla are there?

filt.Phyla.Control.ByTime.aldex.sig <- 
  filt.Phyla.Control.ByTime.aldex[which(filt.Phyla.Control.ByTime.aldex$glm.eBH<0.05),]

length(rownames(filt.Phyla.Control.ByTime.aldex.sig))
## [1] 0

0 sig phyla

Tomato

# subset tomato only samples across all time points, should be n=30
Tomato.Counts.Phyla.filt <- phyla_counts_foraldex %>% 
  select(contains("Tomato"))

ALDEx2 function needs a factor of variables

# results in pigs at different time points being grouped together
Tomato.Counts.Phyla.filt <- Tomato.Counts.Phyla.filt[order(colnames(Tomato.Counts.Phyla.filt))]

# then time point by "alphabetical" where 14 comes before 7
# ex, first few are Pig 10 Day 0, Pig 10 Day 14, Pig 10 Day 7, Pig 1 Day 0, Pig 1 Day 14, etc

TimePoints.Tomato.Phyla <- as.vector(rep(c("Day0", "Day14", "Day7"), times=10))

# check and make sure it looks right
TimePoints.Tomato.Phyla
##  [1] "Day0"  "Day14" "Day7"  "Day0"  "Day14" "Day7"  "Day0"  "Day14" "Day7" 
## [10] "Day0"  "Day14" "Day7"  "Day0"  "Day14" "Day7"  "Day0"  "Day14" "Day7" 
## [19] "Day0"  "Day14" "Day7"  "Day0"  "Day14" "Day7"  "Day0"  "Day14" "Day7" 
## [28] "Day0"  "Day14" "Day7"

More than two conditions this time, use the ANOVA-like test

filt.Phyla.Tomato.ByTime.aldex <- aldex(Tomato.Counts.Phyla.filt, 
                                          TimePoints.Tomato.Phyla, 
                                          mc.samples = 1000, 
                                          test = "kw", 
                                          effect = FALSE)
## aldex.clr: generating Monte-Carlo instances and clr values
## operating in serial mode
## computing center with all features
## aldex.glm: doing Kruskal-Wallace and glm test (ANOVA-like)
## operating in serial mode

We are looking at glm.eBH for the BH corrected ANOVA pval

filt.Phyla.Tomato.ByTime.aldex <- 
  filt.Phyla.Tomato.ByTime.aldex[order(filt.Phyla.Tomato.ByTime.aldex$glm.eBH, 
                                         decreasing = FALSE),]

kable(head(filt.Phyla.Tomato.ByTime.aldex))
kw.ep kw.eBH glm.ep glm.eBH
unclassified (derived from Bacteria) 0.0015220 0.0593092 0.0000096 0.0004317
Tenericutes 0.0051471 0.1075460 0.0061510 0.1147076
Aquificae 0.0368299 0.3521759 0.0264341 0.2773093
Hemichordata 0.1795195 0.5739416 0.1122691 0.4800990
Basidiomycota 0.1334207 0.5773943 0.1081339 0.5509906
Ascomycota 0.1932876 0.6842660 0.0918677 0.5952485
hist(filt.Phyla.Tomato.ByTime.aldex$glm.eBH,
     breaks = 45,
     main = "Histogram of p-values on the effect of time on tomato pigs on phyla",
     xlab = "Benjamini Hochberg corrected p-value (glm.eBH)")

How many significant phyla are there?

filt.Phyla.Tomato.ByTime.aldex.sig <- 
  filt.Phyla.Tomato.ByTime.aldex[which(filt.Phyla.Tomato.ByTime.aldex$glm.eBH<0.05),]

length(rownames(filt.Phyla.Tomato.ByTime.aldex.sig))
## [1] 1

1 sig phyla, unclassified (derived from Bacteria)

---
title: "Goggans et al., INSERT JOURNAL 2022, Microbiome Analysis Code"
author: "Mallory Goggans, Cristian Quiroz-Moreno, Emma Bilbrey and Jessica Cooperstone"
output: 
  html_document:
    toc: true
    toc_float: true
    theme: flatly
    code_download: true
---

# Introduction

INSERT ABSTRACT

Our final taxa used in this anaylsis:
-   included from Bacteria, Archaea, Eukaryota and viruses
-   removed Chordata, Arthropoda, Cnidaria, Porifera, Echinodermata, Streptophyta, Platyhelminthes because they are implausible in our biological system (pig gut microbiome)
-   remove genera/phyla that have more than 20 zeroes/33.33% missing values across our dataset of n=60

In the end: Our final dataset has 45 phyla and 755 genera.

### Load libraries
```{r, message = FALSE}
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

if (!requireNamespace("remotes", quietly = TRUE))
    install.packages("remotes")

if (!requireNamespace("devtools", quietly = TRUE))
    install.packages('devtools')

BiocManager::install("ALDEx2")
BiocManager::install("phyloseq")
remotes::install_github("cpauvert/psadd")
BiocManager::install("multtest")

library(devtools)

devtools::install_github("gauravsk/ranacapa")
```

```{r, message = FALSE}
# analysis packages
library(ALDEx2) # for univariate analysis
library(rstatix) # for ANOVA
library(vegan) # for beta and alpha diversity
library(phyloseq) # for krona plots and rarefaction curves
library(psadd) # additions to phyloseq package for microbiome analysis
library(ranacapa) # Utility Functions  for Simple Environmental Visualizations

# functionality packages
library(data.table) # for nicer transposing
library(here) # for directory management
library(knitr) # for knitting and for kable()
library(tidyverse) # for wrangling and plotting
library(readxl) # for reading Excel files
```

### Set seed

Some of our analyses include permutations, so let's set a seed so we get consistent results each time we run.

```{r}
set.seed(2021) # hoping this seed is better than 2020 :)
```

### Read in metadata

Input files can be found as supplementary information in:

-   UPDATE WITH MALLORY'S PAPER INFO

The data read in chunk below enables loading our data without any outside-of-R handling. In "Metadata" tab of Supplementary Information.

```{r}
# upload metadata
AllSamples.Metadata <- read_excel("../Goggans_etal_2021_tomato_pig_microbiome_WGS.xlsx",
                                       sheet = "TableS2.SampleMetadata")

str(AllSamples.Metadata)

# convert Pig, Diet, Time_Point, Diet_By_Time_Point to factors
# and set levels/order
AllSamples.Metadata$Pig <- as.factor(AllSamples.Metadata$Pig)
AllSamples.Metadata$Diet <- as.factor(AllSamples.Metadata$Diet)
AllSamples.Metadata$Time_Point <- factor(AllSamples.Metadata$Time_Point,
                                         levels = c("Day 0", "Day 7", "Day 14"))
AllSamples.Metadata$Diet_By_Time_Point <- 
  factor(AllSamples.Metadata$Diet_By_Time_Point,
         levels = c("Control Day 0", 
                  "Control Day 7", 
                  "Control Day 14", 
                  "Tomato Day 0", 
                  "Tomato Day 7", 
                  "Tomato Day 14"))

# check
str(AllSamples.Metadata)
```

# Genera-level annotation

Read in genera level data, annotated from MG-RAST. In "Genera" tab of Supplementary Information.

```{r}
Genus.AllSamples.Counts <- read_excel("../Goggans_etal_2021_tomato_pig_microbiome_WGS.xlsx",
                                       sheet = "TableS4.Genera")

str(Genus.AllSamples.Counts)
```

## Data filtering

### Remove inplausible phyla

These phyla are not plausibly found in a rectal swab of a pig, and were incorrectly annotated, so we are removing them.

```{r}
Genus.Counts.Filt <- Genus.AllSamples.Counts %>%
  filter(phylum != "Chordata" , phylum != "Arthropoda" , phylum != "Cnidaria" , 
         phylum != "Porifera" , phylum != "Echinodermata", phylum != "Streptophyta",
         phylum != "Platyhelminthes")
```

Transpose.

```{r}
Genus.Counts.Filt.t <- as.tibble(t(Genus.Counts.Filt))

# make genus colnames
colnames(Genus.Counts.Filt.t) <- Genus.Counts.Filt.t[6,]

# remove domain, phylum, class, order, family
GenusOnly.Counts.Filt.t <- Genus.Counts.Filt.t[7:66,]

# convert character to numeric
GenusOnly.Counts.Filt.t <- as.data.frame(apply((GenusOnly.Counts.Filt.t), 2, as.numeric))

str(GenusOnly.Counts.Filt.t[,1:5])

# add back sample names as column
GenusOnly.Counts.Filt.t <- GenusOnly.Counts.Filt.t %>%
  mutate(Sample_Name = AllSamples.Metadata$Sample_Name)

# move Sample_Name to first column
GenusOnly.Counts.Filt.t <- GenusOnly.Counts.Filt.t %>%
  relocate(Sample_Name)

kable(head(GenusOnly.Counts.Filt.t))
```

Calculate relative abundance, and bind back to metadata.

```{r}
GenusOnly.Counts.Filt.t.wtotal <- GenusOnly.Counts.Filt.t %>%
  mutate(Total.Counts = rowSums(GenusOnly.Counts.Filt.t[,2:ncol(GenusOnly.Counts.Filt.t)]))

dim(GenusOnly.Counts.Filt.t.wtotal)

# create rel abund df
RelAbund.Genus.Filt <- GenusOnly.Counts.Filt.t.wtotal[,2:896]/GenusOnly.Counts.Filt.t.wtotal$Total.Counts

# add back metadata
RelAbund.Genus.Filt <- bind_cols(AllSamples.Metadata, RelAbund.Genus.Filt)
```

### Counting missing data

The goal of these next bits of code are to understand how many missing values we have in our dataset, to set what parameters we will use for filtering.

```{r}
# how many zeros are in the column AHJD-like viruses?
sum(RelAbund.Genus.Filt$`AHJD-like viruses` == 0)  # this code works

# remove metadata   
# metadata is all character or factor, so can select only numeric columns
RelAbund.Genus.Filt.nometadata <- RelAbund.Genus.Filt %>%
  select_if(is.numeric)

# create a list with the number of zeros for each genus
counting_zeros <- sapply(RelAbund.Genus.Filt.nometadata, 
                         function(x){ (sum(x==0))})

# plot a histogram to look at missing values
counting_zeros_df <- as.data.frame(counting_zeros)

hist(counting_zeros_df$counting_zeros, 
     breaks = 61,
     main = "Histogram of Genera with Zero Relative Intensity",
     sub = "Starting at No Zeros",
     xlab = "Number of zero relative intensity values",
     ylab = "Frequency")
```

First column is no missing values, and its so big its hard to see how many missing values we actually have.

```{r}
# plot a histogram to look, but removing genera that are only missing 1 value
counting_zeros_df_missingval <- counting_zeros_df %>%
  rownames_to_column(var = "rowname") %>%
  filter(counting_zeros > 0) %>%
  column_to_rownames(var = "rowname")

# how many genera have at least one missing value?
dim(counting_zeros_df_missingval)
```

186 genera have at least one missing value.

```{r}
# histogram of number of zeros, starting at 1 zero
hist(counting_zeros_df_missingval$counting_zeros, 
     breaks = 60,
     main = "Histogram of Genera with Zero Relative Intensity",
     sub = "Starting at 1 Zero",
     xlab = "Number of zero relative intensity values",
     ylab = "Frequency")

# plot a histogram to look, but removing genera that have 20 or more zeros
counting_zeros_df_missing20ormore <- counting_zeros_df %>%
  rownames_to_column(var = "rowname") %>%
  filter(counting_zeros >= 20) %>%
  column_to_rownames(var = "rowname")

# histogram of number of zeros, starting at 20 zero
hist(counting_zeros_df_missing20ormore$counting_zeros, 
     breaks = 40,
     main = "Histogram of Genera with Zero Relative Intensity",
     sub = "Starting at 20 Zero",
     xlab = "Number of zero relative intensity values",
     ylab = "Frequency")
```

```{r}
# how many genera have 20 or more missing value?
dim(counting_zeros_df_missing20ormore)
```

There are 140 genera that have 20 or more missing values. Because 20 missing values here is 1/3 missing, we decided to use this as our cutoff.

### Filter for <33% missingness

Our decided criteria:\
Filter out genera from relative abundance table that have \> 20 zeros, or more than 33% missing data.

```{r}
# make a character vector of genera names that have > 20 zeros from the rownames in above table
zeros.20 <- c(rownames(counting_zeros_df_missing20ormore))

# filter using this list
RelAbund.Genus.Filt.zerofilt <- RelAbund.Genus.Filt %>%
  rownames_to_column(var = "rowname") %>%
  select(everything(), -all_of(zeros.20)) %>%
  column_to_rownames(var = "rowname")

RelAbund.Genus.Filt.zerofilt[1:3,1:6]

dim(RelAbund.Genus.Filt.zerofilt)
```

Our final dataset has 755 genera (because there are 5 columns of metadata).

Write final dataset genus rel abund to .csv this way we have it.

```{r, eval = FALSE}
write_csv(RelAbund.Genus.Filt.zerofilt,
          file = "Genus_RelAbund_Final_Filtered_WithMetadata.csv")
```

## Microbiome profile

Wrangling to enable collection of some summary statistics about our microbiome profile, including how many genera belong to different domains, etc.

### Wrangling
Grab names of final genera.

```{r}
# contains inplausible genera removed, but not removed for zeroes
dim(Genus.Counts.Filt)
Genus.Counts.Filt[1:5, 1:10]

# final filtered data
RelAbund.Genus.Filt.zerofilt[1:5, 1:10]

# grab colnames which have all the final genera
final_genera <- colnames(RelAbund.Genus.Filt.zerofilt)

# remove metadata colnames
final_genera <- final_genera[6:760]  

final_genera <- as.data.frame(final_genera)

# create a df with the final genera we want to keep for our analysis
final_genera <- final_genera %>%
  rename(genus = final_genera)
```

Get back domain and `inner_join()` with `final_genera` list

```{r}
# pull from full dataset the domain and genus columns
Genus.Counts.Filt.Domain.Genera <- Genus.Counts.Filt %>%
  select(domain, genus)

Genus.Counts.Filt.Domain.Genera[1:10,]

# want to join Genus.Counts.Filt.Domain.Genera with final_genera
final_genera_withdomain <- inner_join(final_genera, Genus.Counts.Filt.Domain.Genera,
                                      by = "genus")
```

### Count genera

```{r}
final_genera_withdomain %>%
  count()

final_genera_withdomain %>%
  group_by(domain) %>%
  count()
```

We have 755 total genera. We have 60 genera from Archaea, 582 from Bacteria, 89 from Eukaryota, and 23 from Viruses.

### Most prevalent genera
What are the most prevalent genera in our pigs?

```{r}
RelAbund.Genus.Filt.zerofilt[1:5, 1:10]

genera_means <- RelAbund.Genus.Filt.zerofilt %>%
  summarize_if(is.numeric, mean)

genera_means_t <- t(genera_means)
genera_means_t <- as.data.frame(genera_means_t)

genera_means_t <- genera_means_t %>%
  rename(rel_abund_genera = V1) %>%
  arrange(-rel_abund_genera)

head(genera_means_t)
```

The most prevalent genera are Prevotella (22.23% average abundance), Bacteroides (10.34%), Clostridium (8.56%), Lactobacillus (6.78%) and Eubacterium (5.16%).

What is the standard deviation of genera with the highest relative abundance?
```{r}
RelAbund.Genus.Filt.zerofilt[1:5, 1:10]

genera_sd <- RelAbund.Genus.Filt.zerofilt %>%
  summarize_if(is.numeric, sd)

genera_sd_t <- t(genera_sd)
genera_sd_t <- as.data.frame(genera_sd_t)

genera_sd_t <- genera_sd_t %>%
  rename(sd_genera = V1) %>%
  arrange(-sd_genera)

head(genera_sd_t)
```
The standard deviations of most prevalent genera are Prevotella (5.4%), Bacteroides (1.9%), Clostridium (1.8%), Lactobacillus (4.6%) and Eubacterium (1.0%).

## Rarefaction curves

### Create tax and OTU tables

This section uses a different package than the rest of the analysis; data and metadata need to be uploaded again and made into format friendly for package.

```{r}
# tax table
TAX_tab <- Genus.AllSamples.Counts %>% 
  select(Domain = domain, Phylum = phylum,
         Class = class, Order = order,
         Family = family, Genus = genus)

tax_names <- colnames(TAX_tab)

head(TAX_tab)
head(tax_names)

#  OTU table
OTU_tab <- Genus.AllSamples.Counts[, seq(7, 66)]

head(OTU_tab)
```

### Create metadata

Since metadata is contained in column names, we will parse them from here.

```{r}
raw_names <- colnames(OTU_tab)
names_table <- data.frame(Raw_names = raw_names)
```

First, the string will split by the middle hyphen.

```{r}
names_table <- names_table %>% 
  separate(Raw_names, into = c("Shotgun", "Type", "Day")) %>% 
  select(-Shotgun)
```

Now, since the character `GutMicrobiome` is constant over all samples, it will be removed. In the same manner, the character `Day` will be removed.

```{r}
names_table <- names_table %>% 
  mutate(Type = str_remove(string = Type, pattern = "GutMicrobiome") ) %>% 
  mutate(Type = str_remove(string = Type, pattern = "GutMicrobime") ) %>% 
  mutate(Day = str_remove(string = Day, pattern = "Day"))
head(names_table, 2)
```

Since `Pig` is in the middle of the sample type and the pig number, it will be used as separator character. And the final result is a tidy data.

```{r message=FALSE, warning=FALSE}
names_table <- names_table %>% 
  separate(col = Type, into = c("Type", "Pig"), sep = "Pig") %>% 
  mutate(Type = factor(Type), Pig = factor(Pig), Day = as.integer(Day)) %>% 
  select(Type, Day, Pig)
head(names_table)
```

### Renaming samples

Now that we have tidy data, it's better to replace long names with shorter ones. New names will be created as *Type_Pig_Day*.  We are also creating names that distinguish the 6 diet by time point groups.

```{r}
tmp_names  <- names_table %>%
  mutate(Pig = paste0("P", Pig), Day = paste0("D", Day)) %>% 
  unite("Kronas", Type:Day) %>% unite("Sample", Kronas:Pig, remove = F) %>% 
  select(-Pig)

head(tmp_names)
```

```{r}
metadata <- bind_cols(names_table, tmp_names)
head(metadata)
```


Finally, in order to use this metadata with the OTU table, which is linked by `names`, **the row names of the metadata and the column names in the OTU table must be the same**.

```{r}
rownames(names_table) <-  tmp_names$Sample
colnames(OTU_tab) <-   tmp_names$Sample
```

### Creating a phyloseq object

It's time create a *phyloseq* object that will allow us to analyze this data easier.

```{r warning=FALSE}
rownames(metadata) <- metadata$Sample
gut_microbiome_raw <- phyloseq(otu_table(OTU_tab, taxa_are_rows = T),
                               tax_table(TAX_tab),
                               sample_data(metadata))

colnames(tax_table(gut_microbiome_raw) ) <- tax_names
```

We can see that data at genus level accounts with 1085 taxas in 60 samples. But, we had developed a filtering scheme to remove very low abundance and inconsistently detected taxa, so let's merge this full list 

```{r}
gut_microbiome_raw
```

### Filtering taxa

We want to only include the taxa we ended up using in our final analysis.  We have already created an df `final_genera` which contains only the genera used in our final analysis.

```{r}
final_genera_forphyloseq <- final_genera$genus

# subset to include only final genera
gut_microbiome_clean <- subset_taxa(gut_microbiome_raw, Genus %in% final_genera_forphyloseq)

gut_microbiome_clean
```

The final phyloseq object has 871 taxonomic levels, in our cases, species since
it is the lowest taxonomic levels that the sequences were annotated.

In order to check if we have the 45 phyla, we are gonna to count the unique 
phyla names in the dataset.

```{r}
length(unique(tax_table(gut_microbiome_clean)[, "Genus"]))
```

We got our 755 genera.

### Creating rarefaction curves

```{r, fig.width = 4, fig.height = 6}
plot_rarefaction <- ranacapa::ggrare(gut_microbiome_clean, step = 60000,
                                   color = 'Type',  se = F, plot = F) 

plot_rarefaction <-  plot_rarefaction + theme_test() +
  facet_wrap("Day", scales = "free_x", ncol = 1, 
             labeller = labeller(Day = c( `0` = "Day 0" ,
                                          `7` = "Day 7",
                                          `14`= "Day 14")) ) + 
  labs(color = "Diet",
       title = "Rarefaction curves") +
  scale_color_manual(values = c( "steelblue2", "tomato2"))
  
plot_rarefaction
```

## Krona plots for exploratory analysis

The `psadd` package is able to create Krona plots with an phyloseq object. Two
Kronas will be created, per sample and per category `Day + Type`. The Krona plots
only include the final filtered taxa we used in our analysis.

```{r eval=FALSE, include=TRUE}
# Write kronas per samples
plot_krona(physeq = gut_microbiome_clean, 
           output = "kronas/per_sample", 
           variable = "Sample")

# Write kronas per category (Sample type + Day) i.e. Tomato_D7
plot_krona(physeq = gut_microbiome_clean, 
           output = "kronas/per_category", 
           variable = "Kronas")
```

## PERMANOVA

Use PERMANOVA to conduct statistical analysis of overall microbial profile differences among groups.

### All samples, full model

Test the overall effect of `Diet`, `Time_Point` and their interaction of the overall microbiome.
ORIGINAL
```{r}
# create factors
factors_time_diet_pig <- RelAbund.Genus.Filt.zerofilt %>% select(Time_Point, Diet, Pig)

# create permutations
perm_time_diet_pig <- how(nperm = 9999)
setBlocks(perm_time_diet_pig) <- with(factors_time_diet_pig, Pig)

# run permanova
AllData.Genus.Filt.permanova <- adonis2(RelAbund.Genus.Filt.zerofilt[,-c(1:5)]~Diet*Time_Point,
                                        data = factors_time_diet_pig,
                                        permutations = perm_time_diet_pig,
                                        method = "bray")

AllData.Genus.Filt.permanova
```

-   Diet: p = 0.0001, significant   
-   Time\_Point: p = 0.0001, significant   
-   Diet\*Time\_Point: p = 0.3831, non-significant   

Comparison when you don't filter out for missing values

```{r}
AllData.Genus.Filt.permanova.no0filt <- adonis2(RelAbund.Genus.Filt[,-c(1:5)]~Diet*Time_Point,
                                        data = factors_time_diet_pig,
                                        permutations = perm_time_diet_pig,
                                        method = "bray")
AllData.Genus.Filt.permanova.no0filt
```

-   Diet: p = 0.0001, significant   
-   Time\_Point: p = 0.0001, significant   
-   Diet\*Time\_Point: p = 0.3750, non-significant   

Significance is the same whether you filter for missing data or not.

NEW
```{r}
set.seed(2021)
# create factors
factors_time_diet_pig_genus <- RelAbund.Genus.Filt.zerofilt %>% select(Time_Point, Diet, Pig)

# create permutations
perm_time_diet_pig_genus <- how(within = Within(type="series", constant=TRUE),
                                plots = Plots(strata=factors_time_diet_pig_genus$Pig,
                                              type="free",))
# run permanova
AllData.Genus.Filt.permanova <- adonis2(RelAbund.Genus.Filt.zerofilt[,-c(1:5)]~Diet*Time_Point,
                                        data = factors_time_diet_pig_genus,
                                        permutations = perm_time_diet_pig_genus,
                                        method = "bray",
                                        by = "margin")

AllData.Genus.Filt.permanova
```

Interaction not significant (p=.355) so remove from model

```{r}
set.seed(2021)
# create factors
factors_time_diet_pig_genus <- RelAbund.Genus.Filt.zerofilt %>% select(Time_Point, Diet, Pig)

# create permutations
perm_time_diet_pig_genus <- how(within = Within(type="series", constant=TRUE),
                                plots = Plots(strata=factors_time_diet_pig_genus$Pig, type="free",))
# run permanova
AllData.Genus.Filt.permanova <- adonis2(RelAbund.Genus.Filt.zerofilt[,-c(1:5)]~Diet+Time_Point,
                                        data = factors_time_diet_pig_genus,
                                        permutations = perm_time_diet_pig_genus,
                                        method = "bray",
                                        by = "margin")

AllData.Genus.Filt.permanova
```

Diet not significant p=.060 but close
Time significant p=.005

Test for homogeneity of multivariate dispersions

```{r}
dis <- vegdist(RelAbund.Genus.Filt.zerofilt[,-c(1:5)], method = "bray")
mod <- betadisper(dis, RelAbund.Genus.Filt.zerofilt$Diet)
permutest(mod)
```

```{r}
dis <- vegdist(RelAbund.Genus.Filt.zerofilt[,-c(1:5)], method = "bray")
mod <- betadisper(dis, RelAbund.Genus.Filt.zerofilt$Time)
permutest(mod)
```

MANOVA TRIAL

```{r, eval = FALSE}
a <- do.call(rbind, lapply(RelAbund.Genus.Filt.zerofilt, as.data.frame))

dep_vars <- cbind(RelAbund.Genus.Filt.zerofilt[-c(1:5)])
fit <- manova(cbind(RelAbund.Genus.Filt.zerofilt$Abiotrophia,RelAbund.Genus.Filt.zerofilt$Acaryochloris)~Diet*Time_Point + (1|Pig), data=RelAbund.Genus.Filt.zerofilt)

tidy(fit)
```

### Post Hoc PERMANOVA within Each Diet

#### Within Control Diet Only

Effect in control diet of time.

```{r}
set.seed(2021)
# filter for only control
control.RelAbund.Genus.Filt.zerofilt <- subset(RelAbund.Genus.Filt.zerofilt, Diet == "Control")

# create factors
factors_control_genera <- droplevels(control.RelAbund.Genus.Filt.zerofilt %>% select(Time_Point, Pig))

# create permutations
perm_control_genera <- how(within = Within(type="series", constant=TRUE),
                                plots = Plots(strata=factors_control_genera$Pig, type="none",))
# run permanova
Control.ByTime.Genus.zerofilt <- adonis2(control.RelAbund.Genus.Filt.zerofilt[,-c(1:5)]~Time_Point,
                                        data = factors_control_genera,
                                        permutations = perm_control_genera,
                                        method = "bray",
                                        by = "margin")

Control.ByTime.Genus.zerofilt
```

Significant effect of time (p = 0.005) within control samples.

Now do pairwise comparisons to see where the significance is coming from

##### Control T1 vs Control T2

```{r}
set.seed(2021)
# filter data set for only samples at T1 and T2
control.T1T2.RelAbund.Genus.Filt.zerofilt <- subset(control.RelAbund.Genus.Filt.zerofilt,
                                               Time_Point != "Day 14")

# create factors
factors_control_T1T2_pig_genus <- droplevels(control.T1T2.RelAbund.Genus.Filt.zerofilt %>%
                                               select(Time_Point, Pig))

# create permutations
perm_control_T1T2_pig_genus <- how(within = Within(type="series", constant=TRUE),
                                   plots = Plots(strata=factors_control_T1T2_pig_genus$Pig,
                                                 type = "free"))

# run PERMANOVA
Control.T1T2.Genus.zerofilt.permanova <- adonis2(control.T1T2.RelAbund.Genus.Filt.zerofilt[,-c(1:5)]~Time_Point,
                                                 data = factors_control_T1T2_pig_genus,
                                                 permutations = perm_control_T1T2_pig_genus, 
                                                 method = "bray",
                                                 by = "margin")

Control.T1T2.Genus.zerofilt.permanova
```

Significant p = .030

##### Control T1 vs T3

```{r}
set.seed(2021)
# filter data set for only samples at T1 and T3
control.T1T3.RelAbund.Genus.Filt.zerofilt <- subset(control.RelAbund.Genus.Filt.zerofilt,
                                               Time_Point != "Day 7")

# create factors
factors_control_T1T3_pig_genus <- droplevels(control.T1T3.RelAbund.Genus.Filt.zerofilt %>%
                                               select(Time_Point, Pig))

# create permutations
perm_control_T1T3_pig_genus <- how(within = Within(type="series", constant=TRUE),
                                   plots = Plots(strata=factors_control_T1T3_pig_genus$Pig,
                                                 type = "free"))

# run PERMANOVA
Control.T1T3.Genus.zerofilt.permanova <- adonis2(control.T1T3.RelAbund.Genus.Filt.zerofilt[,-c(1:5)]~Time_Point,
                                                 data = factors_control_T1T3_pig_genus,
                                                 permutations = perm_control_T1T3_pig_genus, 
                                                 method = "bray",
                                                 by = "margin")

Control.T1T3.Genus.zerofilt.permanova
```

Significant p = .010

##### Control T2 vs T3

```{r}
set.seed(2021)
# filter data set for only samples at T1 and T3
control.T2T3.RelAbund.Genus.Filt.zerofilt <- subset(control.RelAbund.Genus.Filt.zerofilt,
                                               Time_Point != "Day 0")

# create factors
factors_control_T2T3_pig_genus <- droplevels(control.T2T3.RelAbund.Genus.Filt.zerofilt %>%
                                               select(Time_Point, Pig))

# create permutations
perm_control_T2T3_pig_genus <- how(within = Within(type="series", constant=TRUE),
                                   plots = Plots(strata=factors_control_T2T3_pig_genus$Pig,
                                                 type = "free"))

# run PERMANOVA
Control.T2T3.Genus.zerofilt.permanova <- adonis2(control.T2T3.RelAbund.Genus.Filt.zerofilt[,-c(1:5)]~Time_Point,
                                                 data = factors_control_T2T3_pig_genus,
                                                 permutations = perm_control_T2T3_pig_genus, 
                                                 method = "bray",
                                                 by = "margin")

Control.T2T3.Genus.zerofilt.permanova
```

Sig: p=.015

#### Tomato

Effect of tomato diet over time.

```{r}
set.seed(2021)
# filter for only tomato
tomato.RelAbund.Genus.Filt.zerofilt <- subset(RelAbund.Genus.Filt.zerofilt, Diet == "Tomato")

# create factors
factors_tomato_genera <- droplevels(tomato.RelAbund.Genus.Filt.zerofilt %>% select(Time_Point, Pig))

# create permutations
perm_tomato_genera <- how(within = Within(type="series", constant=TRUE),
                          plots = Plots(strata=factors_tomato_genera$Pig, type="none",))
# run permanova
Tomato.ByTime.Genus.zerofilt <- adonis2(tomato.RelAbund.Genus.Filt.zerofilt[,-c(1:5)]~Time_Point,
                                        data = factors_tomato_genera,
                                        permutations = perm_tomato_genera,
                                        method = "bray",
                                        by = "margin")

Tomato.ByTime.Genus.zerofilt
```

Significant effect of time (p = 0.01) within tomato samples

Now do pairwise comparisons to see where the significance is coming from

##### Tomato T1 vs Tomato T2

```{r}
set.seed(2021)
# filter data set for only samples at T1 and T2
tomato.T1T2.RelAbund.Genus.Filt.zerofilt <- subset(tomato.RelAbund.Genus.Filt.zerofilt,
                                               Time_Point != "Day 14")

# create factors
factors_tomato_T1T2_pig_genus <- droplevels(tomato.T1T2.RelAbund.Genus.Filt.zerofilt %>%
                                               select(Time_Point, Pig))

# create permutations
perm_tomato_T1T2_pig_genus <- how(within = Within(type="series", constant=TRUE),
                                   plots = Plots(strata=factors_tomato_T1T2_pig_genus$Pig,
                                                 type = "free"))

# run PERMANOVA
tomato.T1T2.Genus.zerofilt.permanova <- adonis2(tomato.T1T2.RelAbund.Genus.Filt.zerofilt[,-c(1:5)]~Time_Point,
                                                 data = factors_tomato_T1T2_pig_genus,
                                                 permutations = perm_tomato_T1T2_pig_genus, 
                                                 method = "bray",
                                                 by = "margin")

tomato.T1T2.Genus.zerofilt.permanova
```

Not significant .090

##### tomato T1 vs T3

```{r}
set.seed(2021)
# filter data set for only samples at T1 and T3
tomato.T1T3.RelAbund.Genus.Filt.zerofilt <- subset(tomato.RelAbund.Genus.Filt.zerofilt,
                                               Time_Point != "Day 7")

# create factors
factors_tomato_T1T3_pig_genus <- droplevels(tomato.T1T3.RelAbund.Genus.Filt.zerofilt %>%
                                               select(Time_Point, Pig))

# create permutations
perm_tomato_T1T3_pig_genus <- how(within = Within(type="series", constant=TRUE),
                                   plots = Plots(strata=factors_tomato_T1T3_pig_genus$Pig,
                                                 type = "free"))

# run PERMANOVA
tomato.T1T3.Genus.zerofilt.permanova <- adonis2(tomato.T1T3.RelAbund.Genus.Filt.zerofilt[,-c(1:5)]~Time_Point,
                                                 data = factors_tomato_T1T3_pig_genus,
                                                 permutations = perm_tomato_T1T3_pig_genus, 
                                                 method = "bray",
                                                 by = "margin")

tomato.T1T3.Genus.zerofilt.permanova
```

Significant p = .150

##### tomato T2 vs T3

```{r}
set.seed(2021)
# filter data set for only samples at T1 and T3
tomato.T2T3.RelAbund.Genus.Filt.zerofilt <- subset(tomato.RelAbund.Genus.Filt.zerofilt,
                                               Time_Point != "Day 0")

# create factors
factors_tomato_T2T3_pig_genus <- droplevels(tomato.T2T3.RelAbund.Genus.Filt.zerofilt %>%
                                               select(Time_Point, Pig))

# create permutations
perm_tomato_T2T3_pig_genus <- how(within = Within(type="series", constant=TRUE),
                                   plots = Plots(strata=factors_tomato_T2T3_pig_genus$Pig,
                                                 type = "free"))

# run PERMANOVA
tomato.T2T3.Genus.zerofilt.permanova <- adonis2(tomato.T2T3.RelAbund.Genus.Filt.zerofilt[,-c(1:5)]~Time_Point,
                                                 data = factors_tomato_T2T3_pig_genus,
                                                 permutations = perm_tomato_T2T3_pig_genus, 
                                                 method = "bray",
                                                 by = "margin")

tomato.T2T3.Genus.zerofilt.permanova
```

p is non significant =.120

### Subset by time

#### Day 0

Effect of diet at day 0.

```{r}
# filter for day 0 only
d0.RelAbund.Genus.Filt.zerofilt <- subset(RelAbund.Genus.Filt.zerofilt, Time_Point == "Day 0")

# create factors
# don't need to include pig, since no repeated measures here 
# only testing Diet within a time point
factors_day0_genera <- d0.RelAbund.Genus.Filt.zerofilt %>% 
  select(Diet)

# create permutations
perm_day0_genera <- how(nperm = 9999)

# run PERMANOVA
d0.ByTime.Genus.zerofilt <- adonis2(d0.RelAbund.Genus.Filt.zerofilt[,-c(1:5)]~Diet,
                                         data = factors_day0_genera,
                                         permutations = perm_day0_genera,
                                         method = "bray")
d0.ByTime.Genus.zerofilt
```

Non-significant effect of Diet (p = 0.2402) at day 0.

#### Day 7

Effect of diet at day 7.

```{r}
# filter for day 7 only
d7.RelAbund.Genus.Filt.zerofilt <- subset(RelAbund.Genus.Filt.zerofilt, Time_Point == "Day 7")

# create factors
# don't need to include pig, since no repeated measures here 
# only testing Diet within a time point
factors_day7_genera <- d7.RelAbund.Genus.Filt.zerofilt %>% 
  select(Diet)

# create permutations
perm_day7_genera <- how(nperm = 9999)

# run PERMANOVA
d7.ByTime.Genus.zerofilt <- adonis2(d7.RelAbund.Genus.Filt.zerofilt[,-c(1:5)]~Diet,
                                         data = factors_day7_genera,
                                         permutations = perm_day7_genera,
                                         method = "bray")
d7.ByTime.Genus.zerofilt
```

Non-significant effect of Diet (p = 0.2836) at day 7.

#### Day 14

Effect of diet at day 14.

```{r}
# filter for day 14 only
d14.RelAbund.Genus.Filt.zerofilt <- subset(RelAbund.Genus.Filt.zerofilt, Time_Point == "Day 14")

# create factors
# don't need to include pig, since no repeated measures here 
# only testing Diet within a time point
factors_day14_genera <- d14.RelAbund.Genus.Filt.zerofilt %>% 
  select(Diet)

# create permutations
perm_day14_genera <- how(nperm = 9999)

# run PERMANOVA
d14.ByTime.Genus.zerofilt <- adonis2(d14.RelAbund.Genus.Filt.zerofilt[,-c(1:5)]~Diet,
                                     data = factors_day14_genera,
                                     permutations = perm_day14_genera,
                                     method = "bray")
d14.ByTime.Genus.zerofilt
```

Significant effect of Diet (p = 0.005) at day 14.

## PCoA Beta Diversity

#### All samples

```{r}
# calculate distances
genus.filt.dist.20zeros <- vegdist(RelAbund.Genus.Filt.zerofilt[6:ncol(RelAbund.Genus.Filt.zerofilt)], 
                                   method = "bray")

# do multi-dimensional scaling (the PCoA calculations) on those distances
scale.genus.filt.20zeros <- cmdscale(genus.filt.dist.20zeros, k=2)

# make into data frame
scale.genus.filt.df.20zeros <- as.data.frame(cbind(scale.genus.filt.20zeros, 
                                                   AllSamples.Metadata))

# do PCoA again, but get eigen values
scale.genus.filt.20zeros.eig <- cmdscale(genus.filt.dist.20zeros, k=2, eig = TRUE)

# convert eigenvalues to percentages and assign to a variable
eigs.genus.filt.20zeros <- (100* ((scale.genus.filt.20zeros.eig$eig)/(sum(scale.genus.filt.20zeros.eig$eig))))

# round the converted eigenvalues
round.eigs.genus.20zeros <- round(eigs.genus.filt.20zeros, 3)
```

All samples, one PCoA

```{r}
PCoA_genera_20zeros_allsamples <- scale.genus.filt.df.20zeros %>%
ggplot(aes(x = `1`, y = `2`, fill = Diet_By_Time_Point)) +
  geom_point(size=3, color = "black", shape = 21, alpha = 0.9) +
  scale_fill_manual(values=c("skyblue1", "dodgerblue", "royalblue4", "sienna1","firebrick3","tomato4")) +
  theme_classic() +
  theme(axis.text = element_text(color = "black"))+
  labs(x=paste("PC1: ", round.eigs.genus.20zeros[1], "%"), 
       y=paste("PC2: ", round.eigs.genus.20zeros[2], "%"), 
       fill="Diet & Time Point",
       title = "Beta Diversity",
       subtitle = "Genus Level") 

PCoA_genera_20zeros_allsamples
```

```{r, eval = FALSE}
ggsave("Figures/BetaDiversity_PCoA_Genera_allsamples.png", 
       plot = PCoA_genera_20zeros_allsamples, 
       dpi = 800, 
       width = 10, 
       height = 8)
```

Re-level factors

```{r}
scale.genus.filt.df.20zeros <- scale.genus.filt.df.20zeros %>% 
  mutate(Time_Point = fct_relevel(Time_Point, c("Day 0", "Day 7", "Day 14")))
```

##### Facet by time point

```{r}
PCoA_genera_20zeros_facetbytime <- scale.genus.filt.df.20zeros %>%
ggplot(aes(x = `1`, y = `2`, fill = Diet_By_Time_Point)) +
  geom_hline(yintercept = 0, color = "light grey", linetype = "dashed", size = 0.3) +
  geom_vline(xintercept = 0, color = "light grey", linetype = "dashed", size = 0.3) +
  geom_point(size=3, color = "black", shape = 21, alpha = 0.9) +
  scale_fill_manual(values=c("skyblue1", "dodgerblue", "royalblue4", "sienna1","firebrick3","tomato4")) +
  theme_bw() +
  theme(axis.text = element_text(color = "black"),
        strip.background =element_rect(fill="white"),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  labs(x=paste("PC1: ", round.eigs.genus.20zeros[1], "%"), 
       y=paste("PC2: ", round.eigs.genus.20zeros[2], "%"), 
       fill="Diet & Time Point",
       title = "Beta Diversity",
       subtitle = "Genera Level, Subset by Time Point") +
  facet_wrap(~Time_Point)

PCoA_genera_20zeros_facetbytime
```

```{r, eval = FALSE}
ggsave("Figures/BetaDiversity_PCoA_Genera_FacetByTimePoint.png", 
       plot = PCoA_genera_20zeros_facetbytime, 
       dpi = 800, 
       width = 10, 
       height = 6)
```

##### Facet by diet

```{r}
PCoA_genera_20zeros_facetbydiet <- scale.genus.filt.df.20zeros %>%
ggplot(aes(x = `1`, y = `2`, fill = Diet_By_Time_Point)) +
  geom_hline(yintercept = 0, color = "light grey", linetype = "dashed", size = 0.3) +
  geom_vline(xintercept = 0, color = "light grey", linetype = "dashed", size = 0.3) +
  geom_point(size=3, color = "black", shape = 21, alpha = 0.9) +
  scale_fill_manual(values=c("skyblue1", "dodgerblue", "royalblue4", "sienna1","firebrick3","tomato4")) +
  theme_bw() +
  theme(axis.text = element_text(color = "black"),
        strip.background =element_rect(fill="white"),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  labs(x=paste("PC1: ", round.eigs.genus.20zeros[1], "%"), 
       y=paste("PC2: ", round.eigs.genus.20zeros[2], "%"), 
       fill="Diet & Time Point",
       title = "Beta Diversity",
       subtitle = "Genera Level, Subset by Diet") +
  facet_wrap(~Diet)

PCoA_genera_20zeros_facetbydiet
```

```{r, eval = FALSE}
ggsave("Figures/BetaDiversity_PCoA_Genera_FacetByDiet.png", 
       plot = PCoA_genera_20zeros_facetbydiet, 
       dpi = 800, 
       width = 10, 
       height = 6)
```

### Subset

Ended up not using this as part of the paper. Since the input is different here (i.e., the PCoA only has the subset data as an input) the output looks slightly different and we didn't feel this was the most accurate depction of the data. 

#### Control only

```{r}
# calculate distances
control.RelAbund.Genus.Filt.zerofilt.dist <- vegdist(control.RelAbund.Genus.Filt.zerofilt[,-c(1:5)], 
                                                     method = "bray")

# calculate to make PCoA
control.scale.genus.filt.20zeros <- cmdscale(control.RelAbund.Genus.Filt.zerofilt.dist, k=2)

# filter metadata
meta.control <- subset(AllSamples.Metadata, Diet == "Control")

# make into data frame and add metadata
control.scale.genus.filt.20zeros.df <- as.data.frame(cbind(meta.control, control.scale.genus.filt.20zeros))

# get eigenvalues
control.scale.genus.filt.20zeros.eig <- cmdscale(control.RelAbund.Genus.Filt.zerofilt.dist, k=2, eig = TRUE)
control.eigs.genus.filt.20zeros <- (100*((control.scale.genus.filt.20zeros.eig$eig)/(sum(control.scale.genus.filt.20zeros.eig$eig))))
control.round.eigs.genus.20zeros <- round(control.eigs.genus.filt.20zeros, 3)
```

Reset factor levels

```{r}
control.scale.genus.filt.20zeros.df$Time_Point <- factor(control.scale.genus.filt.20zeros.df$Time_Point, levels = c("Day 0", "Day 7", "Day 14"))
```

Plot

```{r}
control.scale.genus.filt.20zeros.df %>%
ggplot(aes(x = `1`, y = `2`, fill = Time_Point)) +
  geom_point(size=3, shape = 21, color = "black", alpha = 0.9) +
  scale_fill_manual(values=c("skyblue1", "dodgerblue", "royalblue4"))+
  theme_classic() +
  theme(axis.text = element_text(color = "black")) +
  labs(x=(paste(control.round.eigs.genus.20zeros[1], "%")), 
       y=(paste(control.round.eigs.genus.20zeros[2], "%")), 
       fill = "Time Point",
       title = "Beta Diversity",
       subtitle = "Genera Level, Control Samples Only")
```

#### Tomato only

```{r}
# calculate distances
tomato.RelAbund.Genus.Filt.zerofilt.dist <- vegdist(tomato.RelAbund.Genus.Filt.zerofilt[,-c(1:5)], method = "bray")

# calculate to make PCoA
tomato.scale.genus.filt.20zeros <- cmdscale(tomato.RelAbund.Genus.Filt.zerofilt.dist, k=2)

# filter metadata
meta.tomato <- subset(AllSamples.Metadata, Diet == "Tomato")

# make into data frame and add metadata
tomato.scale.genus.filt.20zeros.df <- as.data.frame(cbind(meta.tomato, tomato.scale.genus.filt.20zeros))

# get eigenvalues
tomato.scale.genus.filt.20zeros.eig <- cmdscale(tomato.RelAbund.Genus.Filt.zerofilt.dist, k=2, eig = TRUE)
tomato.eigs.genus.filt.20zeros <- (100*((tomato.scale.genus.filt.20zeros.eig$eig)/(sum(tomato.scale.genus.filt.20zeros.eig$eig))))
tomato.round.eigs.genus.20zeros <- round(tomato.eigs.genus.filt.20zeros, 3)
```

Reset factor levels

```{r}
tomato.scale.genus.filt.20zeros.df$Time_Point <- factor(tomato.scale.genus.filt.20zeros.df$Time_Point, levels = c("Day 0", "Day 7", "Day 14"))
```

Plot

```{r}
tomato.scale.genus.filt.20zeros.df %>%
ggplot(aes(x = `1`, y = `2`, fill = Time_Point))+
  geom_point(size=3, shape = 21, color = "black", alpha = 0.9) +
  scale_fill_manual(values = c("sienna1","firebrick3","tomato4"))+
  theme_classic() +
  theme(axis.text = element_text(color = "black")) +
  labs(x=(paste(tomato.round.eigs.genus.20zeros[1], "%")), 
       y=(paste(tomato.round.eigs.genus.20zeros[2], "%")), 
       fill = "Time Point",
       title = "Beta Diversity",
       subtitle = "Genera Level, Tomato Samples Only")
```

#### Day 0 Only

```{r}
# calculate distances
d0.RelAbund.Genus.Filt.zerofilt.dist <- vegdist(d0.RelAbund.Genus.Filt.zerofilt[,-c(1:5)], method = "bray")

# calculate to make PCoA
d0.scale.genus.filt.20zeros <- cmdscale(d0.RelAbund.Genus.Filt.zerofilt.dist, k=2)

# filter metadata
meta.day0 <- subset(AllSamples.Metadata, Time_Point == "Day 0")

# make into data frame and add metadata
d0.scale.genus.filt.20zeros.df <- as.data.frame(cbind(meta.day0, d0.scale.genus.filt.20zeros))

# get eigenvalues
d0.scale.genus.filt.20zeros.eig <- cmdscale(d0.RelAbund.Genus.Filt.zerofilt.dist, k=2, eig = TRUE)
d0.eigs.genus.filt.20zeros <- (100*((d0.scale.genus.filt.20zeros.eig$eig)/(sum(d0.scale.genus.filt.20zeros.eig$eig))))
d0.round.eigs.genus.20zeros <- round(d0.eigs.genus.filt.20zeros, 3)
```

Plot

```{r}
d0.scale.genus.filt.20zeros.df %>%
ggplot(aes( x= `1`, y = `2`, fill = Diet))+
  geom_point(size=3, shape = 21, color = "black", alpha = 0.9) +
  scale_fill_manual(values = c("steelblue2", "tomato2")) +
  theme_classic() +
  theme(axis.text = element_text(color = "black")) +
  labs(x=(paste(d0.round.eigs.genus.20zeros[1], "%")), 
       y=(paste(d0.round.eigs.genus.20zeros[2], "%")),
       title = "Beta Diversity",
       subtitle = "Genera Level, Day 0 Only")
```

#### Day 7 Only

```{r}
# calculate distances
d7.RelAbund.Genus.Filt.zerofilt.dist <- vegdist(d7.RelAbund.Genus.Filt.zerofilt[,-c(1:5)], method = "bray")

# calculate to make PCoA
d7.scale.genus.filt.20zeros <- cmdscale(d7.RelAbund.Genus.Filt.zerofilt.dist, k=2)

# filter metadata
meta.day7 <- subset(AllSamples.Metadata, Time_Point == "Day 7")

# make into data frame and add metadata
d7.scale.genus.filt.20zeros.df <- as.data.frame(cbind(meta.day7, d7.scale.genus.filt.20zeros))

# get eigenvalues
d7.scale.genus.filt.20zeros.eig <- cmdscale(d7.RelAbund.Genus.Filt.zerofilt.dist, k=2, eig = TRUE)
d7.eigs.genus.filt.20zeros <- (100*((d7.scale.genus.filt.20zeros.eig$eig)/(sum(d7.scale.genus.filt.20zeros.eig$eig))))
d7.round.eigs.genus.20zeros <- round(d7.eigs.genus.filt.20zeros, 3)
```

Plot

```{r}
d7.scale.genus.filt.20zeros.df %>%
ggplot(aes( x= `1`, y = `2`, fill = Diet))+
  geom_point(size=3, shape = 21, color = "black", alpha = 0.9) +
  scale_color_manual(values = c("steelblue2", "tomato2")) +
  theme_classic() +
  theme(axis.text = element_text(color = "black")) +
  labs(x=(paste(d7.round.eigs.genus.20zeros[1], "%")), 
       y=(paste(d7.round.eigs.genus.20zeros[2], "%")),
       title = "Beta Diversity",
       subtitle = "Genera Level, Day 7 Only")
```

#### Day 14 Only

```{r}
# calculate distances
d14.RelAbund.Genus.Filt.zerofilt.dist <- vegdist(d14.RelAbund.Genus.Filt.zerofilt[,-c(1:5)], method = "bray")
# calculate to make PCoA
d14.scale.genus.filt.20zeros <- cmdscale(d14.RelAbund.Genus.Filt.zerofilt.dist, k=2)

# filter metadata
meta.day14 <- subset(AllSamples.Metadata, Time_Point == "Day 14")

# make into data frame and add metadata
d14.scale.genus.filt.20zeros.df <- as.data.frame(cbind(meta.day14, d14.scale.genus.filt.20zeros))

# get eigenvalues
d14.scale.genus.filt.20zeros.eig <- cmdscale(d14.RelAbund.Genus.Filt.zerofilt.dist, k=2, eig = TRUE)
d14.eigs.genus.filt.20zeros <- (100*((d14.scale.genus.filt.20zeros.eig$eig)/(sum(d14.scale.genus.filt.20zeros.eig$eig))))
d14.round.eigs.genus.20zeros <- round(d14.eigs.genus.filt.20zeros, 3)
```

Plot

```{r}
d14.scale.genus.filt.20zeros.df %>%
ggplot(aes( x= `1`, y = `2`, fill = Diet))+
  geom_point(size=3, shape = 21, color = "black", alpha = 0.9) +
  scale_color_manual(values = c("steelblue2", "tomato2")) +
  theme_classic() +
  theme(axis.text = element_text(color = "black")) +
  labs(x=(paste(d14.round.eigs.genus.20zeros[1], "%")), 
       y=(paste(d0.round.eigs.genus.20zeros[2], "%")),
       title = "Beta Diversity",
       subtitle = "Genera Level, Day 14 Only")
```

## Alpha Diversity

### Wrangling

```{r}
kable(head(RelAbund.Genus.Filt.zerofilt))
```

```{r}
# move Sample_Name to rownames, remove metadata
RelAbund.Genus.Filt.zerofilt.alphadiv <- RelAbund.Genus.Filt.zerofilt

rownames(RelAbund.Genus.Filt.zerofilt.alphadiv) <- RelAbund.Genus.Filt.zerofilt.alphadiv$Sample_Name  

RelAbund.Genus.Filt.zerofilt.alphadiv[1:5,1:8]

# remove metadata
RelAbund.Genus.Filt.zerofilt.alphadiv <- RelAbund.Genus.Filt.zerofilt.alphadiv %>%
  select(Abiotrophia:ncol(.))

RelAbund.Genus.Filt.zerofilt.alphadiv[1:5,1:5]

rownames(RelAbund.Genus.Filt.zerofilt.alphadiv)
```

### Calculate alpha diversity

```{r}
# run alpha diversity on phyla
genera.filt.div <- diversity(RelAbund.Genus.Filt.zerofilt.alphadiv, index = "shannon")

# convert to df
genera.filt.div.df <- as.data.frame(genera.filt.div)

# make column name 'shannon.phyla.filt'
colnames(genera.filt.div.df) <- "shannon.genera.filt"

head(genera.filt.div.df)
```

Combine shannon alpha diversity results with metadata

```{r}
# compile genera metadata
genera.metadata <- RelAbund.Genus.Filt.zerofilt[,1:5]

# combine with metadata
genera.filt.div.df.meta <- cbind(genera.metadata, genera.filt.div.df)

head(genera.filt.div.df.meta)
```

### Plotting

X axis by diet

```{r}
alpha.diversity.genera.bydiet <- genera.filt.div.df.meta %>%
  ggplot(aes(x = Diet, y = shannon.genera.filt, fill = Diet_By_Time_Point)) +
  geom_boxplot(outlier.shape = NA) +
  geom_point(aes(fill = Diet_By_Time_Point), color = "black", alpha = 0.7, position=position_jitterdodge()) +
  scale_fill_manual(values = c("skyblue1", "dodgerblue", "royalblue4", 
                               "sienna1","firebrick3","tomato4")) +
  scale_color_manual(values = c("skyblue1", "dodgerblue", "royalblue4", 
                               "sienna1","firebrick3","tomato4")) +
  theme_minimal() +
  theme(axis.text.x = element_text(size = 12, color = "black")) +
  labs(x=NULL, 
       y="Shannon diversity index", 
       title = "Alpha Diversity",
       subtitle = "Shannon Index, Genera Level", 
       fill="Diet & Time Point")

alpha.diversity.genera.bydiet
```

```{r, eval = FALSE}
ggsave("Figures/AlphaDiversityGenera_ByDiet_Boxplot.png", 
       plot = alpha.diversity.genera.bydiet, 
       dpi = 800, 
       width = 10, 
       height = 6)
```

X-axis by Day

```{r}
genera.filt.div.df.meta <- genera.filt.div.df.meta %>%
  mutate(Time_Point = fct_relevel(Time_Point, c("Day 0", "Day 7", "Day 14")))

alpha.diversity.genera.bytime <- genera.filt.div.df.meta %>%
  ggplot(aes(x = Time_Point, y = shannon.genera.filt, fill = Diet_By_Time_Point)) +
  geom_boxplot(outlier.shape = NA) +
  geom_point(aes(fill = Diet_By_Time_Point), color = "black", alpha = 0.7, position=position_jitterdodge()) +
  scale_fill_manual(values = c("skyblue1", "dodgerblue", "royalblue4", 
                               "sienna1","firebrick3","tomato4")) +
  scale_color_manual(values = c("skyblue1", "dodgerblue", "royalblue4", 
                               "sienna1","firebrick3","tomato4")) +
  theme_minimal() +
  theme(axis.text.x = element_text(size = 12, color = "black")) +
  labs(x=NULL, 
       y="Shannon diversity index", 
       title = "Alpha Diversity",
       subtitle = "Shannon Index, Genera Level", 
       fill="Diet & Time Point")

alpha.diversity.genera.bytime
```

```{r, eval = FALSE}
ggsave("Figures/AlphaDiversityGenera_ByTime_Boxplot.png", 
       plot = alpha.diversity.genera.bytime, 
       dpi = 800, 
       width = 7, 
       height = 5)
```

### Statistics

Repeated measures ANOVA on Shannon alpha diversity

```{r}
# must remove columns that aren't used in anova
head(genera.filt.div.df.meta) 

genera.filt.div.df.meta.foranova <- genera.filt.div.df.meta[,-c(1,5)]

head(genera.filt.div.df.meta.foranova)

genera.filt.alphadiv.anova <- 
  anova_test(data = genera.filt.div.df.meta.foranova,
             formula = shannon.genera.filt ~ Diet*Time_Point + Error(Pig/Time_Point),
             dv = shannon.genera.filt, 
             wid = Pig, 
             within = Time_Point, 
             between = Diet)

get_anova_table(genera.filt.alphadiv.anova)
```

-   Non-significant effect of diet (p = 0.106)   
-   Non-significant effect of timepoint (0.777)   
-   Non-significant interaction of diet:time point (p = 0.964).   

Check for normality

```{r}
shapiro.test(genera.filt.div.df.meta.foranova$shannon.genera.filt)
```

Normal.

No need for posthoc test since no model parameters are significant.

## ALDEx2

Quick introduction to anatomy of the aldex function 

The aldex function does every step - data transformation and statistics   
variable.name \<- aldex(reads.data, variables.vector, mc.samples=\#, test="t"/"kw", effect=T/F)   
reads.data - your reads/count data, unchanged    
variables.vector - a vector of the variables corresponding to sample groups, in SAME order as sample names (and therefore columns)   
mc.samples - here you tell the function how many Monte Carlo sampels to use with an integer (128 is typical)   
test - which test do you want, t-test and wilcoxon, or anova-like and kruskal wallace? (will always do the parametric and non-parametric) t = t-test and wilcoxon kw = anova-like and kruskal wallace   
effect - do you want to incude effect results in output?

Key to aldex outputs - taken directly from vignette

-   we.ep - Expected P value of Welch's t test
-   we.eBH - Expected Benjamini-Hochberg corrected P value of Welch's t test   
-   wi.ep - Expected P value of Wilcoxon rank test
-   wi.eBH - Expected Benjamini-Hochberg corrected P value of Wilcoxon test
-   kw.ep - Expected P value of Kruskal-Wallace test
-   kw.eBH - Expected Benjamini-Hochberg corrected P value of Kruskal-Wallace test
-   glm.ep - Expected P value of glm test
-   glm.eBH - Expected Benjamini-Hochberg corrected P value of glm test
-   rab.all - median clr value for all samples in the feature
-   rab.win.NS - median clr value for the NS group of samples
-   rab.win.S - median clr value for the S group of samples
-   dif.btw - median difference in clr values between S and NS groups
-   dif.win - median of the largest difference in clr values within S and NS groups
-   effect - median effect size: diff.btw / max(diff.win) for all instances
-   overlap - proportion of effect size that overlaps 0 (i.e. no effect)

ALDEx2 takes counts, not relative abundance.

We are using Benjamini Hochberg corrected pvalues, or `we.eBH` for t-tests (i.e., subsetting by time), and Benjamini-Hochberg corrected pvalues of the glm test `glm.eBH` for ANOVA tests (i.e., subsetting by diet)

Downloading ALDEx2

```{r, eval = FALSE}
if (!requireNamespace("BiocManager", quietly = TRUE))
  install.packages("BiocManager")

BiocManager::install("ALDEx2")
```

### Wrangling

Since we use counts for ALDEx2, we need to filter our counts data to include only the genera we ended up using in our final analysis

```{r}
# this is the data set filtered to remove inplausible phyla, but still includes genera with a lot of missing values 
Genus.Counts.Filt[1:10,1:10]
dim(Genus.Counts.Filt)

# final genera list (after filtering for zeros)
final_genera[1:10,]

# how many final genera do we have?
dim(final_genera)

# join to create a df with genera in rows, samples in columns
# filtered for genera used in this analysis
genera_counts_foraldex <- inner_join(final_genera, Genus.Counts.Filt,
                                     by = "genus")

dim(genera_counts_foraldex)

# remove non-necessary metadata
genera_counts_foraldex <- genera_counts_foraldex[,-c(2:6)]

genera_counts_foraldex[1:10, 1:4]

# add genera as rownames
rownames(genera_counts_foraldex) <- genera_counts_foraldex$genus

# remove genera as column for cleaner data
genera_counts_foraldex <- genera_counts_foraldex %>%
  select(-genus)
```

### Subset by Time

#### Day 0

Look at the effect of diet at day 0.

```{r}
# subset day 0 only
Day0.Counts.Genera.filt <- genera_counts_foraldex %>% 
  select(ends_with("Day0"))
```

ALDEx2 function needs a factor of variables

```{r}
# order alphabetically so making the meta data vector is easier
Day0.Counts.Genera.filt <- Day0.Counts.Genera.filt[order(colnames(Day0.Counts.Genera.filt))]

Diets.Day0.Genera <- as.vector(c(rep("Control", times=10), rep("Tomato", times=10)))

# check and make sure it came out right
Diets.Day0.Genera
```

Run t-test

```{r, cache = TRUE}
filt.Genera.Day0.ByDiet.aldex <- aldex(Day0.Counts.Genera.filt, 
                                       Diets.Day0.Genera, 
                                       mc.samples = 1000, 
                                       test = "t", 
                                       effect = TRUE)
```

```{r}
filt.Genera.Day0.ByDiet.aldex <- 
  filt.Genera.Day0.ByDiet.aldex[order(filt.Genera.Day0.ByDiet.aldex$we.eBH, 
                                      decreasing = FALSE),]

kable(head(filt.Genera.Day0.ByDiet.aldex))
```

Create a histogram of pvalues of `we.eBH`

```{r}
hist(filt.Genera.Day0.ByDiet.aldex$we.eBH,
     breaks = 20,
     main = "Histogram of p-values on the effect of diet at day 0 on genera",
     xlab = "Benjamini Hochberg corrected p-value (we.eBH)")
```

`we.eBH` is the Benjamini-Hochberg corrected p-value, no significantly different genera at day 0.

#### Day 7

Look at the effect of diet at day 7.

```{r}
# subset day 7 only
Day7.Counts.Genera.filt <- genera_counts_foraldex %>% 
  select(ends_with("Day7"))
```

ALDEx2 function needs a factor of variables

```{r}
# order alphabetically so making the meta data vector is easier
Day7.Counts.Genera.filt <- Day7.Counts.Genera.filt[order(colnames(Day7.Counts.Genera.filt))]

Diets.Day7.Genera <- as.vector(c(rep("Control", times=10), rep("Tomato", times=10)))

# check and make sure it came out right
Diets.Day7.Genera
```

Run t-test

```{r, cache = TRUE}
filt.Genera.Day7.ByDiet.aldex <- aldex(Day7.Counts.Genera.filt, 
                                       Diets.Day7.Genera, 
                                       mc.samples = 1000, 
                                       test = "t", 
                                       effect = TRUE)
```

```{r}
filt.Genera.Day7.ByDiet.aldex <- 
  filt.Genera.Day7.ByDiet.aldex[order(filt.Genera.Day7.ByDiet.aldex$we.eBH, 
                                      decreasing = FALSE),]

kable(head(filt.Genera.Day7.ByDiet.aldex))
```

One genera was significantly different by diet at day 7 - unclassified (derived from bacteria), padj = 0.025

```{r}
hist(filt.Genera.Day7.ByDiet.aldex$we.eBH,
     breaks = 20,
     main = "Histogram of p-values on the effect of diet at day 7 on genera",
     xlab = "Benjamini Hochberg corrected p-value (we.eBH)")
```

What is the directionality of the change?

```{r}
filt.Genera.Day7.ByDiet.aldex %>%
  select(rab.win.Control, rab.win.Tomato, we.eBH) %>%
  filter(we.eBH <= 0.05)
```

Unclassified (derived from Bacteria) is higher in Tomato than Control.

#### Day 14

Look at the effect of diet on day 14.

```{r}
# subset day 14 only
Day14.Counts.Genera.filt <- genera_counts_foraldex %>% 
  select(ends_with("Day14"))
```

ALDEx2 function needs a factor of variables

```{r}
# order alphabetically so making the meta data vector is easier
Day14.Counts.Genera.filt <- Day14.Counts.Genera.filt[order(colnames(Day14.Counts.Genera.filt))]

Diets.Day14.Genera <- as.vector(c(rep("Control", times=10), rep("Tomato", times=10)))

# check and make sure it came out right
Diets.Day14.Genera
```

Run t-test

```{r, cache = TRUE}
filt.Genera.Day14.ByDiet.aldex <- aldex(Day14.Counts.Genera.filt, 
                                       Diets.Day14.Genera, 
                                       mc.samples = 1000, 
                                       test = "t", 
                                       effect = TRUE)
```

```{r}
filt.Genera.Day14.ByDiet.aldex <- 
  filt.Genera.Day14.ByDiet.aldex[order(filt.Genera.Day14.ByDiet.aldex$we.eBH, 
                                      decreasing = FALSE),]

kable(head(filt.Genera.Day14.ByDiet.aldex))
```

```{r}
hist(filt.Genera.Day14.ByDiet.aldex$we.eBH,
     breaks = 20,
     main = "Histogram of p-values on the effect of diet at day 14 on genera",
     xlab = "Benjamini Hochberg corrected p-value (we.eBH)")
```

How many significant genera are there?

```{r}
filt.Day14.Genera.aldex.sig <- filt.Genera.Day14.ByDiet.aldex[which(filt.Genera.Day14.ByDiet.aldex$we.eBH<0.05),]

length(rownames(filt.Day14.Genera.aldex.sig))
```

Which genera are they?

```{r}
sig_day14_genera_aldex2 <- as.data.frame(cbind(rownames(filt.Day14.Genera.aldex.sig),
                                 filt.Day14.Genera.aldex.sig$we.eBH))

sig_day14_genera_aldex2 <- sig_day14_genera_aldex2 %>%
  rename(Genera = V1,
         we.eBH_pvalue = V2)

sig_day14_genera_aldex2
```

* Lambda-like viruses
* Staphylococcus
* Alphatorquervirus
* unclassified (derived from Bacteria)
* Loa
* Plasmodium
* Propionibacterium
* Saccharomyces, Stenotrophomonas
* Malassezia
* Roseiflexus
* Brugia
* Strepococcus
* Vanderwaltozyma

What is the directionality of the change?

```{r}
filt.Genera.Day14.ByDiet.aldex %>%
  select(rab.win.Control, rab.win.Tomato, we.eBH) %>%
  filter(we.eBH <= 0.05)
```

All significantly different genera are higher in tomato as compared to control.


### Subset by diet

#### Control

```{r}
# subset control only samples across all time points, should be n=30
Control.Counts.Genera.filt <- genera_counts_foraldex %>% 
  select(contains("Control"))

dim(Control.Counts.Genera.filt)
```

ALDEx2 function needs a factor of variables

```{r}
# results in pigs at different time points being grouped together
Control.Counts.Genera.filt <- Control.Counts.Genera.filt[order(colnames(Control.Counts.Genera.filt))]

# then time point by "alphabetical" where 14 comes before 7
# ex, first few are Pig 10 Day 0, Pig 10 Day 14, Pig 10 Day 7, Pig 1 Day 0, Pig 1 Day 14, etc
TimePoints.Control.Genera <- as.vector(rep(c("Day0", "Day14", "Day7"), times=10))

# check and make sure it looks right
TimePoints.Control.Genera
```

More than two conditions this time, use the ANOVA-like test, Kruskal Wallis

```{r, cache = TRUE}
filt.Genera.Control.ByTime.aldex <- aldex(Control.Counts.Genera.filt, 
                                          TimePoints.Control.Genera, 
                                          mc.samples = 1000, 
                                          test = "kw", 
                                          effect = FALSE)
```

We are looking at `glm.eBH` for the BH corrected ANOVA pvalue

```{r}
filt.Genera.Control.ByTime.aldex <- 
  filt.Genera.Control.ByTime.aldex[order(filt.Genera.Control.ByTime.aldex$glm.eBH, 
                                         decreasing = FALSE),]

kable(head(filt.Genera.Control.ByTime.aldex))
```

```{r}
hist(filt.Genera.Control.ByTime.aldex$glm.eBH,
     breaks = 20,
     main = "Histogram of p-values on the effect of time within the control diet on genera",
     xlab = "Benjamini Hochberg corrected p-value (glm.eBH)")
```

How many significantly different genera are there?

```{r}
filt.Genera.Control.ByTime.aldex.sig <- 
  filt.Genera.Control.ByTime.aldex[which(filt.Genera.Control.ByTime.aldex$glm.eBH<0.05),]

length(rownames(filt.Genera.Control.ByTime.aldex.sig))
```

4 sig genera

Which genera are they?

```{r}
sig_control_genera_aldex2 <- as.data.frame(cbind(rownames(filt.Genera.Control.ByTime.aldex.sig),
                                 filt.Genera.Control.ByTime.aldex.sig$glm.eBH))

sig_control_genera_aldex2 <- sig_control_genera_aldex2 %>%
  rename(Genera = V1,
         glm.eBH_pval = V2)

sig_control_genera_aldex2
```

* Oribacterium
* Streptococcus
* Lactococcus
* Granulicatella

#### Tomato

```{r}
# subset tomato only samples across all time points, should be n=30
Tomato.Counts.Genera.filt <- genera_counts_foraldex %>% 
  select(contains("Tomato"))
```

ALDEx2 function needs a factor of variables

```{r}
# results in pigs at different time points being grouped together
Tomato.Counts.Genera.filt <- Tomato.Counts.Genera.filt[order(colnames(Tomato.Counts.Genera.filt))]

# then time point by "alphabetical" where 14 comes before 7
# ex, first few are Pig 10 Day 0, Pig 10 Day 14, Pig 10 Day 7, Pig 1 Day 0, Pig 1 Day 14, etc
TimePoints.Tomato.Genera <- as.vector(rep(c("Day0", "Day14", "Day7"), times=10))

# check and make sure it looks right
TimePoints.Tomato.Genera
```

More than two conditions this time, use the ANOVA-like test

```{r, cache = TRUE}
filt.Genera.Tomato.ByTime.aldex <- aldex(Tomato.Counts.Genera.filt, 
                                          TimePoints.Tomato.Genera, 
                                          mc.samples = 1000, 
                                          test = "kw", 
                                          effect = FALSE)
```

We are looking at `glm.eBH` for the BH corrected ANOVA pvalue

```{r}
filt.Genera.Tomato.ByTime.aldex <- 
  filt.Genera.Tomato.ByTime.aldex[order(filt.Genera.Tomato.ByTime.aldex$glm.eBH, 
                                         decreasing = FALSE),]

kable(head(filt.Genera.Tomato.ByTime.aldex))
```

```{r}
hist(filt.Genera.Tomato.ByTime.aldex$glm.eBH,
     breaks = 20,
     main = "Histogram of p-values on the effect of time within the tomato diet on genera",
     xlab = "Benjamini Hochberg corrected p-value (glm.eBH)")
```

How many significantly different genera are there?

```{r}
filt.Genera.Tomato.ByTime.aldex.sig <- 
  filt.Genera.Tomato.ByTime.aldex[which(filt.Genera.Tomato.ByTime.aldex$glm.eBH<0.05),]

length(rownames(filt.Genera.Tomato.ByTime.aldex.sig))
```

4 sig genera

Which genera are they?

```{r}
sig_tomato_genera_aldex2 <- as.data.frame(cbind(rownames(filt.Genera.Tomato.ByTime.aldex.sig),
                                 filt.Genera.Tomato.ByTime.aldex.sig$glm.eBH))

sig_tomato_genera_aldex2 <- sig_tomato_genera_aldex2 %>%
  rename(Genera = V1,
         glm.eBH_pval = V2)

sig_tomato_genera_aldex2
```

* Staphylococcus
* Alphatorquevirus
* Lambda-like viruses
* unclassified (derived from Bacteria)

Any overlap between sig differences at day 14 and by diet?

Control over time and day 14 overlap

```{r}
intersect(sig_day14_genera_aldex2$Genera, sig_control_genera_aldex2$Genera)
```

Streptococcus

Tomato over time and day 14 overlap

```{r}
intersect(sig_day14_genera_aldex2$Genera, sig_tomato_genera_aldex2$Genera)
```

* Lambda-like viruses
* Staphylococcus
* Alphatorquevirus
* unclassified (derived from Bacteria)

# Phyla-level annotation

Read in phyla level data, annotated from MG-RAST. In "Phyla" tab of Supplementary Information.

```{r}
Phyla.Counts <- read_excel("../Goggans_etal_2021_tomato_pig_microbiome_WGS.xlsx",
                                       sheet = "TableS3.Phyla")

str(Phyla.Counts)
```

## Data filtering

### Remove inplausible phyla

These phyla are not plausibly found in a rectal swab of a pig, and were incorrectly annotated, so we are removing them.

```{r}
Phyla.Counts.Filt <- Phyla.Counts %>%
  filter(phylum != "Chordata" , phylum != "Arthropoda" , phylum != "Cnidaria" , 
         phylum != "Porifera" , phylum != "Echinodermata", phylum != "Streptophyta",
         phylum != "Platyhelminthes")
```

Transpose.

```{r}
Phyla.Counts.Filt.t <- as.tibble(t(Phyla.Counts.Filt))

# make phyla colnames
colnames(Phyla.Counts.Filt.t) <- Phyla.Counts.Filt.t[2,]

# remove domain, phylum rows
Phyla.Counts.Filt.t <- Phyla.Counts.Filt.t[3:62,]

# convert character to numeric
Phyla.Counts.Filt.t <- as.data.frame(apply((Phyla.Counts.Filt.t), 2, as.numeric))

str(Phyla.Counts.Filt.t[,1:5])

# add back sample names as column
Phyla.Counts.Filt.t <- Phyla.Counts.Filt.t %>%
  mutate(Sample_Name = AllSamples.Metadata$Sample_Name)

# move Sample_Name to first column
Phyla.Counts.Filt.t <- Phyla.Counts.Filt.t %>%
  relocate(Sample_Name)

kable(head(Phyla.Counts.Filt.t))
```

Calculate relative abundance, and bind back to metadata.

```{r}
Phyla.Counts.Filt.t.wtotal <- Phyla.Counts.Filt.t %>%
  mutate(Total.Counts = rowSums(Phyla.Counts.Filt.t[,2:ncol(Phyla.Counts.Filt.t)]))

dim(Phyla.Counts.Filt.t.wtotal)

# create rel abund df
RelAbund.Phyla.Filt <- Phyla.Counts.Filt.t.wtotal[,2:54]/Phyla.Counts.Filt.t.wtotal$Total.Counts

# add back metadata
RelAbund.Phyla.Filt <- bind_cols(AllSamples.Metadata, RelAbund.Phyla.Filt)
```

### Counting missing data

```{r}
# remove metadata
RelAbund.Phyla.Filt.nometadata <- RelAbund.Phyla.Filt %>%
  select_if(is.numeric) 

# create a list with the number of zeros for each genus
counting_zeros_phyla <- sapply(RelAbund.Phyla.Filt.nometadata, function(x){ (sum(x==0))})

# plot a histogram to look
counting_zeros_phyla_df <- as.data.frame(counting_zeros_phyla)

hist(counting_zeros_phyla_df$counting_zeros_phyla, 
     breaks = 61,
     main = "Histogram of Genera with Zero Relative Intensity",
     sub = "Starting at No Zeros",
     xlab = "Number of zero relative intensity values",
     ylab = "Frequency")
```

Big first bar is many phyla which have zero missing values.

```{r}
# filter for any phyla with at least 1 missing value
counting_zeros_phyla_df_missingval <- counting_zeros_phyla_df %>%
  rownames_to_column(var = "rowname") %>%
  filter(counting_zeros_phyla > 0) %>%
  column_to_rownames(var = "rowname")

# how many genera have at least one missing value?
dim(counting_zeros_phyla_df_missingval)
```

9 phyla have at least 1 missing value.

```{r}
# histogram of number of zeros, starting at 1 zero
hist(counting_zeros_phyla_df_missingval$counting_zeros_phyla, 
     breaks = 60,
     main = "Histogram of Genera with Zero Relative Intensity",
     sub = "Starting at 1 Zero",
     xlab = "Number of zero relative intensity values",
     ylab = "Frequency")
```

```{r}
# create table of number of phyla with more than 1 missing value
counting_zeros_phyla_df_missingval
```

### Filter for <33% missingness

This would mean 33% missing values in our dataset.

```{r}
# removing phyla that have 20 or more zeros
counting_zeros_phyla_df_missing20ormore <- counting_zeros_phyla_df %>%
  rownames_to_column(var = "rowname") %>%
  filter(counting_zeros_phyla >= 20) %>%
  column_to_rownames(var = "rowname")

# how many phyla have 20 or more missing value?
dim(counting_zeros_phyla_df_missing20ormore)
```

8 phyla have more than 20 missing values.

```{r}
# make a character vector from the rownames of previous data frame containing the phyla we want to get rid of
phyla.20zeros <- c(rownames(counting_zeros_phyla_df_missing20ormore))

# use select function to select all columns EXCEPT the ones in the character vector, we want to remove those
# and add in metadata
RelAbund.Phyla.Filt.zerofilt <- RelAbund.Phyla.Filt %>%
  select(everything(), -all_of(phyla.20zeros))

# check dimensions to make sure it filtered correctly
dim(RelAbund.Phyla.Filt.zerofilt)
# removed 8, like we expected
```

Our final dataset has 45 phyla (because 5 columns are metadata).

Write final dataset genus rel abund to csv

```{r, eval = FALSE}
write_csv(RelAbund.Phyla.Filt.zerofilt,
          file = "Phyla_RelAbund_Final_Filtered_WithMetadata.csv")
```

## Microbiome profile

See "Genera" section above for rarefaction curves and kronas plots

### Wrangling

Wrangling to enable collection of some summary statistics about our microbiome profile.

Grab names of final phyla

```{r}
# contains inplausible genera removed, but not removed for zeroes
dim(Phyla.Counts.Filt)
Phyla.Counts.Filt[1:10, 1:5]

# final filtered data
RelAbund.Phyla.Filt.zerofilt[1:10, 1:5]
dim(RelAbund.Phyla.Filt.zerofilt)

# grab colnames which have all the final phyla
final_phyla <- colnames(RelAbund.Phyla.Filt.zerofilt)

final_phyla

# remove metadata colnames
final_phyla <- final_phyla[6:50]  

final_phyla <- as.data.frame(final_phyla)

final_phyla <- final_phyla %>%
  rename(phylum = final_phyla)
```

Get back domain and `inner_join` with `final_phyla` list

```{r}
# pull from full dataset the domain and genus columns
Phyla.Counts.Filt.Domain.Phyla <- Phyla.Counts.Filt %>%
  select(domain, phylum)

head(Phyla.Counts.Filt.Domain.Phyla)

# want to join Genus.Counts.Filt.Domain.Genera with final_phyla
final_phyla_withdomain <- inner_join(final_phyla, Phyla.Counts.Filt.Domain.Phyla,
                                     by = "phylum")
```

### Count phyla

```{r}
final_phyla_withdomain %>%
  count()

final_phyla_withdomain %>%
  group_by(domain) %>%
  count()
```

### Most prevalent phyla

```{r}
RelAbund.Phyla.Filt.zerofilt[1:5, 1:10]

phyla_means <- RelAbund.Phyla.Filt.zerofilt %>%
  summarize_if(is.numeric, mean)

phyla_means_t <- t(phyla_means)
phyla_means_t <- as.data.frame(phyla_means_t)

phyla_means_t %>%
  rename(rel_abund_phyla = V1) %>%
  arrange(-rel_abund_phyla)
```

The most prevalent phyla are Firmicutes (52.7% average abundance), Bacteroidetes (35.4%), Actinobacteria (4.7%), Proteobacteria (3.9%) and Fusobaceria (0.43%).

What is the standard deviation of phyla with the highest relative abundance?
```{r}
RelAbund.Phyla.Filt.zerofilt[1:5, 1:10]

phyla_sd <- RelAbund.Phyla.Filt.zerofilt %>%
  summarize_if(is.numeric, sd)

phyla_sd_t <- t(phyla_sd)
phyla_sd_t <- as.data.frame(phyla_sd_t)

phyla_sd_t <- phyla_sd_t %>%
  rename(sd_phyla = V1) %>%
  arrange(-sd_phyla)

head(phyla_sd_t)
```

The standard deviations of most prevalent phyla are Firmicutes (5.5% average abundance), Bacteroidetes (5.9%), Actinobacteria (1.8%), Proteobacteria (1.2%) and Fusobaceria (8.5 x 10^4%).


What percent of the reads are from Bacteria?

```{r}
final_phyla_bacteriaonly <- final_phyla_withdomain %>%
  filter(domain == "Bacteria")

final_phyla_bacteriaonly <- final_phyla_bacteriaonly$phylum

# select columns corresponding to bacteria
RelAbund.Phyla.Filt.zerofilt.baconly <- RelAbund.Phyla.Filt.zerofilt %>%
  select(contains(final_phyla_bacteriaonly)) 

# create rowsums
RelAbund.Phyla.Filt.zerofilt.baconly <- RelAbund.Phyla.Filt.zerofilt.baconly %>%
  mutate(rowsums = rowSums(RelAbund.Phyla.Filt.zerofilt.baconly[])) 

mean(RelAbund.Phyla.Filt.zerofilt.baconly$rowsums)
sd(RelAbund.Phyla.Filt.zerofilt.baconly$rowsums)

```

## PERMANOVA

### All samples, full model

Repeated measures, using Pig as a block and set permutations using `how()`
ORIGINAL BLOCK
```{r}

set.seed(2021)
# create factors
factors_time_diet_pig_phyla <- RelAbund.Phyla.Filt.zerofilt %>% 
  select(Time_Point, Diet, Pig)

# create permutations
perm_time_diet_pig_phyla <- how(nperm = 9999)
setBlocks(perm_time_diet_pig_phyla) <- with(factors_time_diet_pig_phyla, Pig)

# run permanova
AllData.Phyla.Filt.permanova <- adonis2(RelAbund.Phyla.Filt.zerofilt[,-c(1:5)]~Diet*Time_Point,
                                        data = factors_time_diet_pig_phyla,
                                        permutations = perm_time_diet_pig_phyla,
                                        method = "bray")

AllData.Phyla.Filt.permanova
```

-   Diet: p = 0.0150, significant   
-   Time\_Point: p = 0.0054, significant   
-   Diet\*Time\_Point: p = 0.4870, non-significant   

Interaction:

```{r}
# create factors
Pig <- as.factor(RelAbund.Phyla.Filt.zerofilt$Pig)
Diet <- as.factor(RelAbund.Phyla.Filt.zerofilt$Diet)


# create permutations
perm_time_diet_pig_phyla <- how(within = Within(type="series", constant=TRUE),
                                plots = Plots(strata=Pig, type="free",))
# run permanova
AllData.Phyla.Filt.permanova <- adonis2(RelAbund.Phyla.Filt.zerofilt[,-c(1:5)]~Diet*Time_Point,
                                        data = factors_time_diet_pig_phyla,
                                        permutations = perm_time_diet_pig_phyla,
                                        method = "bray",
                                        by = "margin")

AllData.Phyla.Filt.permanova
```

Interaction not significant (p=.51), so remove from model

```{r}
# create factors
Pig <- as.factor(RelAbund.Phyla.Filt.zerofilt$Pig)
Diet <- as.factor(RelAbund.Phyla.Filt.zerofilt$Diet)


# create permutations
perm_time_diet_pig_phyla <- how(within = Within(type="series", constant=TRUE),
                                plots = Plots(strata=Pig, type = "free"))
# run permanova
AllData.Phyla.Filt.permanova <- adonis2(RelAbund.Phyla.Filt.zerofilt[,-c(1:5)]~Diet + Time_Point,
                                        data = factors_time_diet_pig_phyla,
                                        permutations = perm_time_diet_pig_phyla,
                                        method = "bray",
                                        by = "margin")

AllData.Phyla.Filt.permanova
```

Test for homogeneity of multivariate dispersions

```{r}
dis <- vegdist(RelAbund.Phyla.Filt.zerofilt[,-c(1:5)], method = "bray")
mod <- betadisper(dis, Diet)
permutest(mod)
```
Non significant! good for our PERMANOVA test validity

### Post Hoc PERMANOVA within Time

#### Within Control Diet Only

Effect of control diet over time.

```{r}
# filter data set for only control samples
control.RelAbund.Phyla.zerofilt <- subset(RelAbund.Phyla.Filt.zerofilt, Diet == "Control")

# create factors
factors_control_pig_phyla <- droplevels(control.RelAbund.Phyla.zerofilt %>% 
  select(Time_Point, Pig))

# create permutations
perm_control_pig_phyla <- how(within = Within(type="series", constant=TRUE),
                                plots = Plots(strata=factors_control_pig_phyla$Pig, type = "free"))

# run PERMANOVA
Control.ByTime.Phyla.zerofilt.permanova <- adonis2(control.RelAbund.Phyla.zerofilt[,-c(1:5)]~Time_Point,
        data = factors_control_pig_phyla,
        permutations = perm_control_pig_phyla, 
        method = "bray",
        by = "margin")

Control.ByTime.Phyla.zerofilt.permanova
```

Significant effect of time (p = 0.005) within control samples. Beta diversity changing with time. Now the question is where is the difference coming from (ie. between which time points?)

##### Control T1 vs Control T2

```{r}
# filter data set for only samples at T1 and T2
control.T1T2.RelAbund.Phyla.zerofilt <- subset(control.RelAbund.Phyla.zerofilt, Time_Point != "Day 14")

# create factors
factors_control_T1T2_pig_phyla <- droplevels(control.T1T2.RelAbund.Phyla.zerofilt %>% 
  select(Time_Point, Pig))

# create permutations
perm_control_T1T2_pig_phyla <- how(within = Within(type="series", constant=TRUE),
                                   plots = Plots(strata=factors_control_T1T2_pig_phyla$Pig,
                                                 type = "free"))

# run PERMANOVA
Control.T1T2.Phyla.zerofilt.permanova <- adonis2(control.T1T2.RelAbund.Phyla.zerofilt[,-c(1:5)]~Time_Point,
        data = factors_control_T1T2_pig_phyla,
        permutations = perm_control_T1T2_pig_phyla, 
        method = "bray",
        by = "margin")

Control.T1T2.Phyla.zerofilt.permanova
```
p=.085 so not significant between T1 and T2

##### Control T1 vs Control T3

```{r}
# filter data set for only samples at T1 and T3
control.T1T3.RelAbund.Phyla.zerofilt <- subset(control.RelAbund.Phyla.zerofilt, Time_Point != "Day 7")

# create factors
factors_control_T1T3_pig_phyla <- droplevels(control.T1T3.RelAbund.Phyla.zerofilt %>% 
  select(Time_Point, Pig))

# create permutations
perm_control_T1T3_pig_phyla <- how(within = Within(type="series", constant=TRUE),
                                   plots = Plots(strata=factors_control_T1T3_pig_phyla$Pig,
                                                 type = "free"))

# run PERMANOVA
Control.T1T3.Phyla.zerofilt.permanova <- adonis2(control.T1T3.RelAbund.Phyla.zerofilt[,-c(1:5)]~Time_Point,
        data = factors_control_T1T3_pig_phyla,
        permutations = perm_control_T1T3_pig_phyla, 
        method = "bray",
        by = "margin")

Control.T1T3.Phyla.zerofilt.permanova
```

P = .02 so significant. There is a significant difference between T1 and T3 in the control diet pigs

##### Control T2 vs Control T3

```{r}
# filter data set for only samples at T2 and T3
control.T2T3.RelAbund.Phyla.zerofilt <- subset(control.RelAbund.Phyla.zerofilt, Time_Point != "Day 0")

# create factors
factors_control_T2T3_pig_phyla <- droplevels(control.T2T3.RelAbund.Phyla.zerofilt %>% 
  select(Time_Point, Pig))

# create permutations
perm_control_T2T3_pig_phyla <- how(within = Within(type="series", constant=TRUE),
                                   plots = Plots(strata=factors_control_T2T3_pig_phyla$Pig,
                                                 type = "free"))

# run PERMANOVA
Control.T2T3.Phyla.zerofilt.permanova <- adonis2(control.T2T3.RelAbund.Phyla.zerofilt[,-c(1:5)]~Time_Point,
        data = factors_control_T2T3_pig_phyla,
        permutations = perm_control_T2T3_pig_phyla, 
        method = "bray",
        by = "margin")

Control.T2T3.Phyla.zerofilt.permanova
```

P = .315 so not significant

#### Within Tomato Diet Only

Effect of tomato diet over time.

```{r}
# filter data for only tomato samples
tomato.RelAbund.Phyla.zerofilt <- subset(RelAbund.Phyla.Filt.zerofilt, Diet == "Tomato")

# create factors
factors_tomato_pig_phyla <- tomato.RelAbund.Phyla.zerofilt %>% 
  select(Time_Point, Pig)

# create permutations
perm_tomato_pig_phyla <- how(within = Within(type="series", constant=TRUE),
                             plots = Plots(strata=factors_tomato_pig_phyla$Pig, type = "free"))

# run PERMANOVA
tomato.ByTime.Phyla.zerofilt.permanova <- adonis2(tomato.RelAbund.Phyla.zerofilt[,-c(1:5)]~Time_Point,
                                                  data = factors_tomato_pig_phyla,
                                                  permutations = perm_tomato_pig_phyla, 
                                                  method = "bray",
                                                  by = "margin")

tomato.ByTime.Phyla.zerofilt.permanova
```

Non-significant effect of time (p = 0.325) within tomato samples. So no post hoc tests necessary.

### Subset by time

#### Day 0 Only

Effect of diet at day 0.

```{r}
# filter data set for only day 0 samples
d0.RelAbund.Phyla.zerofilt <- subset(RelAbund.Phyla.Filt.zerofilt, Time_Point == "Day 0")

# create factors
# don't need to include pig, since no repeated measures here 
# only testing Diet within a time point
factors_day0_phyla <- d0.RelAbund.Phyla.zerofilt %>% 
  select(Diet)

# create permutations
perm_day0_phyla <- how(nperm = 9999)

# run PERMANOVA
d0.Phyla.zerofilt.permanova <- adonis2(d0.RelAbund.Phyla.zerofilt[,-c(1:5)]~Diet,
                                       data = factors_day0_phyla,
                                       permutations = perm_day0_phyla, 
                                       method = "bray")

d0.Phyla.zerofilt.permanova
```

Non-significant effect of diet (p=0.376) at day 0.

#### Day 7 Only

Effect of diet at day 7.

```{r}
# filter data set for only day 7 samples
d7.RelAbund.Phyla.zerofilt <- subset(RelAbund.Phyla.Filt.zerofilt, Time_Point == "Day 7")

# create factors
# don't need to include pig, since no repeated measures here 
# only testing Diet within a time point
factors_day7_phyla <- d7.RelAbund.Phyla.zerofilt %>% 
  select(Diet)

# create permutations
perm_day7_phyla <- how(nperm = 9999)

# run PERMANOVA
d7.Phyla.zerofilt.permanova <- adonis2(d7.RelAbund.Phyla.zerofilt[,-c(1:5)]~Diet,
                                       data = factors_day7_phyla,
                                       permutations = perm_day7_phyla, 
                                       method = "bray")

d7.Phyla.zerofilt.permanova
```

Non-significant effect of diet (p=0.4097) at day 7.

#### Day 14 Only

Effect of diet at day 14.

```{r}
# filter data set for only day 14 samples
d14.RelAbund.Phyla.zerofilt <- subset(RelAbund.Phyla.Filt.zerofilt, Time_Point == "Day 14")

# create factors
# don't need to include pig, since no repeated measures here 
# only testing Diet within a time point
factors_day14_phyla <- d14.RelAbund.Phyla.zerofilt %>% 
  select(Diet)

# create permutations
perm_day14_phyla <- how(nperm = 9999)

# run PERMANOVA
d14.Phyla.zerofilt.permanova <- adonis2(d14.RelAbund.Phyla.zerofilt[,-c(1:5)]~Diet,
                                       data = factors_day14_phyla,
                                       permutations = perm_day14_phyla, 
                                       method = "bray")

d14.Phyla.zerofilt.permanova
```

Non-significant effect of diet (p=0.256) at day 14.

## PCoA Beta Diversity

### All samples

```{r}
# calculate distances
phyla.filt.dist.zeros <- vegdist(RelAbund.Phyla.Filt.zerofilt[6:ncol(RelAbund.Phyla.Filt.zerofilt)], 
                                 method = "bray")

# do multi-dimensional scaling (the PCoA calculations) on those distances
scale.phyla.filt.zerofilt <- cmdscale(phyla.filt.dist.zeros, k=2)

# make into data frame and bind metadata
scale.phyla.filt.zerofilt.df <- as.data.frame(cbind(scale.phyla.filt.zerofilt, AllSamples.Metadata))

# do PCoA again, but get eigen values
scale.phyla.filt.zerofilt.eig <- cmdscale(phyla.filt.dist.zeros, k=2, eig = TRUE)

# convert eigenvalues to percentages and assign to a variable
eigs.phyla.filt.zerofilt <- (100*((scale.phyla.filt.zerofilt.eig$eig)/(sum(scale.phyla.filt.zerofilt.eig$eig))))

# round the converted eigenvalues
round.eigs.phyla.filt.zerofilt <- round(eigs.phyla.filt.zerofilt, 3)
```

Plot

```{r}
PCoA_phyla_20zeros_allsamples <- scale.phyla.filt.zerofilt.df %>%
ggplot(aes(x=`1`, y=`2`, fill = Diet_By_Time_Point)) +
  geom_point(size = 3, color = "black", shape = 21, alpha = 0.9) +
  scale_fill_manual(values=c("skyblue1", "dodgerblue", "royalblue4", "sienna1","firebrick3","tomato4")) +
  theme_classic() +
  theme(axis.text = element_text(color = "black"))+
  labs(x=paste("PC1: ", round.eigs.phyla.filt.zerofilt[1], "%"), 
       y=paste("PC2: ", round.eigs.phyla.filt.zerofilt[2], "%"), 
       fill="Diet & Time Point",
       title = "Beta Diversity",
       subtitle = "Phyla Level") 

PCoA_phyla_20zeros_allsamples
```

```{r, eval = FALSE}
ggsave("Figures/BetaDiversity_PCoA_Phyla_allsamples.png", 
       plot = PCoA_phyla_20zeros_allsamples, 
       dpi = 800, 
       width = 10, 
       height = 8)
```

#### Facet by time point

Re-level factors

```{r}
scale.phyla.filt.zerofilt.df <- scale.phyla.filt.zerofilt.df %>% 
  mutate(Time_Point = fct_relevel(Time_Point, c("Day 0", "Day 7", "Day 14")))
```

```{r}
PCoA_phyla_20zeros_facetbytime <- scale.phyla.filt.zerofilt.df %>%
ggplot(aes(x=`1`, y=`2`, fill = Diet_By_Time_Point)) +
  geom_hline(yintercept = 0, color = "light grey", linetype = "dashed", size = 0.3) +
  geom_vline(xintercept = 0, color = "light grey", linetype = "dashed", size = 0.3) +
  geom_point(size = 3, color = "black", shape = 21, alpha = 0.9) +
  scale_fill_manual(values=c("skyblue1", "dodgerblue", "royalblue4", "sienna1","firebrick3","tomato4")) +
  theme_bw() +
  theme(axis.text = element_text(color = "black"),
        strip.background =element_rect(fill="white"),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  labs(x=paste("PC1: ", round.eigs.phyla.filt.zerofilt[1], "%"), 
       y=paste("PC2: ", round.eigs.phyla.filt.zerofilt[2], "%"), 
       fill="Diet & Time Point",
       title = "Beta Diversity",
       subtitle = "Phyla Level, Subset by Time Point") +
  facet_wrap(~Time_Point)

PCoA_phyla_20zeros_facetbytime
```

```{r, eval = FALSE}
ggsave("Figures/BetaDiversity_PCoA_Phyla_FacetByTimePoint.png", 
       plot = PCoA_phyla_20zeros_facetbytime, 
       dpi = 800, 
       width = 10, 
       height = 6)
```

#### Facet by diet

```{r}
PCoA_phyla_20zeros_facetbydiet <- scale.phyla.filt.zerofilt.df %>%
ggplot(aes(x=`1`, y=`2`, fill = Diet_By_Time_Point)) +
  geom_hline(yintercept = 0, color = "light grey", linetype = "dashed", size = 0.3) +
  geom_vline(xintercept = 0, color = "light grey", linetype = "dashed", size = 0.3) +
  geom_point(size = 3, color = "black", shape = 21, alpha = 0.9) +
  scale_fill_manual(values=c("skyblue1", "dodgerblue", "royalblue4", "sienna1","firebrick3","tomato4")) +
  theme_bw() +
  theme(axis.text = element_text(color = "black"),
        strip.background =element_rect(fill="white"),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  labs(x=paste("PC1: ", round.eigs.phyla.filt.zerofilt[1], "%"), 
       y=paste("PC2: ", round.eigs.phyla.filt.zerofilt[2], "%"), 
       fill="Diet & Time Point",
       title = "Beta Diversity",
       subtitle = "Phyla Level, Subset by Diet") +
  facet_wrap(~Diet)

PCoA_phyla_20zeros_facetbydiet
```

```{r, eval = FALSE}
ggsave("Figures/BetaDiversity_PCoA_Phyla_FacetByDiet.png", 
       plot = PCoA_phyla_20zeros_facetbydiet, 
       dpi = 800, 
       width = 10, 
       height = 8)
```

### Subset

Ended up not using this as part of the paper. Since the input is different here (i.e., the PCoA only has the subset data as an input) the output looks slightly different. 

#### Control only

```{r}
# calculate distances
control.phyla.filt.dist.zeros <- vegdist(control.RelAbund.Phyla.zerofilt[,-c(1:5)], method = "bray")

# do PCoA calculations
control.scale.phyla.filt.zerofilt <- cmdscale(control.phyla.filt.dist.zeros, k=2)

# filter meta data
meta.control <- subset(AllSamples.Metadata, Diet == "Control")

# make pcoa table into data frame and bind metadata to it
control.scale.phyla.filt.zerofilt.df <- as.data.frame(cbind(meta.control, control.scale.phyla.filt.zerofilt))

# do PCoA again, but get eigenvalues
control.scale.phyla.filt.zerofilt.eig <- cmdscale(control.phyla.filt.dist.zeros, k=2, eig = TRUE)

# convert eigenvalues to percentages and assign to a variable
control.eigs.phyla.filt.zerofilt <- (100*((control.scale.phyla.filt.zerofilt.eig$eig)/sum(control.scale.phyla.filt.zerofilt.eig$eig)))

# round the eigenvalues
round.control.eigs.phyla.filt.zerofilt <- round(control.eigs.phyla.filt.zerofilt, 3)
```

Re-level factors

```{r}
control.scale.phyla.filt.zerofilt.df$Time_Point <- factor(control.scale.phyla.filt.zerofilt.df$Time_Point, 
                                                          levels = c("Day 0", "Day 7", "Day 14"))
```

Plot

```{r}
control.scale.phyla.filt.zerofilt.df %>%
ggplot(aes(x=`1`, y=`2`, fill = Time_Point)) +
  geom_point(size=3, shape = 21, color = "black", alpha = 0.9) +
  scale_fill_manual(values=c("skyblue1", "dodgerblue", "royalblue4")) +
  theme_classic() +
  theme(axis.text = element_text(color = "black")) +
  labs(x=paste("PC1: ", round.control.eigs.phyla.filt.zerofilt[1], "%"), 
       y=paste("PC2: ", round.control.eigs.phyla.filt.zerofilt[2], "%"), 
       fill="Time Point",
       title = "Beta Diversity",
       subtitle = "Phyla Level, Control Only")
```

#### Tomato only

```{r}
# calculate distances
tomato.phyla.filt.dist.zeros <- vegdist(tomato.RelAbund.Phyla.zerofilt[,-c(1:5)], method = "bray")

# do PCoA calculations
tomato.scale.phyla.filt.zerofilt <- cmdscale(tomato.phyla.filt.dist.zeros, k=2)

# filter meta data
meta.tomato <- subset(AllSamples.Metadata, Diet == "Tomato")

# make pcoa table into data frame and bind metadata to it
tomato.scale.phyla.filt.zerofilt.df <- as.data.frame(cbind(meta.tomato, tomato.scale.phyla.filt.zerofilt))

# do PCoA again, but get eigenvalues
tomato.scale.phyla.filt.zerofilt.eig <- cmdscale(tomato.phyla.filt.dist.zeros, k=2, eig = TRUE)

# convert eigenvalues to percentages and assign to a variable
tomato.eigs.phyla.filt.zerofilt <- (100*((tomato.scale.phyla.filt.zerofilt.eig$eig)/sum(tomato.scale.phyla.filt.zerofilt.eig$eig)))

# round the eigenvalues
round.tomato.eigs.phyla.filt.zerofilt <- round(tomato.eigs.phyla.filt.zerofilt, 3)
```

Re-level factors

```{r}
tomato.scale.phyla.filt.zerofilt.df$Time_Point <- factor(tomato.scale.phyla.filt.zerofilt.df$Time_Point, 
                                                         levels = c("Day 0", "Day 7", "Day 14"))
```

Plot

```{r}
tomato.scale.phyla.filt.zerofilt.df %>%
ggplot(aes(x=`1`, y=`2`, fill = Time_Point)) +
  geom_point(size=3, shape = 21, color = "black", alpha = 0.9) +
  scale_fill_manual(values=c("sienna1","firebrick3","tomato4")) +
  theme_classic() +
  theme(axis.text = element_text(color = "black")) +
  labs(x=paste("PC1: ", round.tomato.eigs.phyla.filt.zerofilt[1], "%"), 
       y=paste("PC2: ", round.tomato.eigs.phyla.filt.zerofilt[2], "%"), 
       fill="Time Point",
       title = "Beta Diversity",
       subtitle = "Phyla Level, Tomato Only")
```

#### Day 0 Only

```{r}
# calculate distances
d0.phyla.filt.dist.zeros <- vegdist(d0.RelAbund.Phyla.zerofilt[,-c(1:5)], method = "bray")

# do PCoA calculations
d0.scale.phyla.filt.zerofilt <- cmdscale(d0.phyla.filt.dist.zeros, k=2)

# filter meta data
meta.d0 <- subset(AllSamples.Metadata, Time_Point == "Day 0")

# make pcoa table into data frame and bind metadata to it
d0.scale.phyla.filt.zerofilt.df <- as.data.frame(cbind(meta.d0, d0.scale.phyla.filt.zerofilt))

# do PCoA again, but get eigenvalues
d0.scale.phyla.filt.zerofilt.eig <- cmdscale(d0.phyla.filt.dist.zeros, k=2, eig = TRUE)

# convert eigenvalues to percentages and assign to a variable
d0.eigs.phyla.filt.zerofilt <- (100*((d0.scale.phyla.filt.zerofilt.eig$eig)/sum(d0.scale.phyla.filt.zerofilt.eig$eig)))

# round the eigenvalues
round.d0.eigs.phyla.filt.zerofilt <- round(d0.eigs.phyla.filt.zerofilt, 3)
```

Plot

```{r}
d0.scale.phyla.filt.zerofilt.df %>%
  ggplot(aes(x = `1`, y = `2`, fill = Diet)) +
  geom_point(size=3, shape = 21, color = "black", alpha = 0.9) +
  scale_fill_manual(values=c("steelblue2", "tomato2")) +
  theme_classic() +
  theme(axis.text = element_text(color = "black")) +
  labs(x=paste("PC1: ", round.d0.eigs.phyla.filt.zerofilt[1], "%"), 
       y=paste("PC2: ", round.d0.eigs.phyla.filt.zerofilt[2], "%"), 
       fill="Time Point",
       title = "Beta Diversity",
       subtitle = "Phyla Level, Day 0 Only")
```

#### Day 7 Only

```{r}
# calculate distances
d7.phyla.filt.dist.zeros <- vegdist(d7.RelAbund.Phyla.zerofilt[,-c(1:5)], method = "bray")

# do PCoA calculations
d7.scale.phyla.filt.zerofilt <- cmdscale(d7.phyla.filt.dist.zeros, k=2)

# filter meta data
meta.d7 <- subset(AllSamples.Metadata, Time_Point == "Day 7")

# make pcoa table into data frame and bind metadata to it
d7.scale.phyla.filt.zerofilt.df <- as.data.frame(cbind(meta.d7, d7.scale.phyla.filt.zerofilt))

# do PCoA again, but get eigenvalues
d7.scale.phyla.filt.zerofilt.eig <- cmdscale(d7.phyla.filt.dist.zeros, k=2, eig = TRUE)

# convert eigenvalues to percentages and assign to a variable
d7.eigs.phyla.filt.zerofilt <- (100*((d7.scale.phyla.filt.zerofilt.eig$eig)/sum(d7.scale.phyla.filt.zerofilt.eig$eig)))

# round the eigenvalues
round.d7.eigs.phyla.filt.zerofilt <- round(d7.eigs.phyla.filt.zerofilt, 3)
```

Plot

```{r}
d7.scale.phyla.filt.zerofilt.df %>%
  ggplot(aes(x = `1`, y = `2`, fill = Diet)) +
  geom_point(size=3, shape = 21, color = "black", alpha = 0.9) +
  scale_fill_manual(values=c("steelblue2", "tomato2")) +
  theme_classic() +
  theme(axis.text = element_text(color = "black")) +
  labs(x=paste("PC1: ", round.d7.eigs.phyla.filt.zerofilt[1], "%"), 
       y=paste("PC2: ", round.d7.eigs.phyla.filt.zerofilt[2], "%"), 
       fill="Time Point",
       title = "Beta Diversity",
       subtitle = "Phyla Level, Day 7 Only")
```

#### Day 14 Only

```{r}
# calculate distances
d14.phyla.filt.dist.zeros <- vegdist(d14.RelAbund.Phyla.zerofilt[,-c(1:5)], method = "bray")
# do PCoA calculations
d14.scale.phyla.filt.zerofilt <- cmdscale(d14.phyla.filt.dist.zeros, k=2)

# filter meta data
meta.d14 <- subset(AllSamples.Metadata, Time_Point == "Day 14")

# make pcoa table into data frame and bind metadata to it
d14.scale.phyla.filt.zerofilt.df <- as.data.frame(cbind(meta.d14, d14.scale.phyla.filt.zerofilt))

# do PCoA again, but get eigenvalues
d14.scale.phyla.filt.zerofilt.eig <- cmdscale(d14.phyla.filt.dist.zeros, k=2, eig = TRUE)

# convert eigenvalues to percentages and assign to a variable
d14.eigs.phyla.filt.zerofilt <- (100*((d14.scale.phyla.filt.zerofilt.eig$eig)/sum(d14.scale.phyla.filt.zerofilt.eig$eig)))

# round the eigenvalues
round.d14.eigs.phyla.filt.zerofilt <- round(d14.eigs.phyla.filt.zerofilt, 3)
```

Plot

```{r}
d14.scale.phyla.filt.zerofilt.df %>%
  ggplot(aes(x = `1`, y = `2`, fill = Diet)) +
  geom_point(size=3, shape = 21, color = "black", alpha = 0.9) +
  scale_fill_manual(values=c("steelblue2", "tomato2")) +
  theme_classic() +
  theme(axis.text = element_text(color = "black")) +
  labs(x=paste("PC1: ", round.d14.eigs.phyla.filt.zerofilt[1], "%"), 
       y=paste("PC2: ", round.d14.eigs.phyla.filt.zerofilt[2], "%"), 
       fill="Time Point",
       title = "Beta Diversity",
       subtitle = "Phyla Level, Day 14 Only")
```

## Bacteroidota/Bacteriodetes, Bacilotta/Firmicutes, and their ratio

Given a priori interest in the phyla Bacteroidota/Bacteriodetes and Bacilotta/Firmicutes, we are conducted repeated measures ANOVA analysis for their changes in our samples. The ratio of Bacteroidota to Bacilotta is a commonly used metric for assessing the health of the microbiome, with a higher Bacteroidota to Bacilotta (formerly B to F) ratio being more beneficial.

### Wrangling

```{r}
dim(RelAbund.Phyla.Filt.zerofilt)
```

60 samples, and 45 phyla (5 columns are metadata).

Re-level `Time_Point`

```{r}
RelAbund.Phyla.Filt.zerofilt <- RelAbund.Phyla.Filt.zerofilt %>%
  mutate(Time_Point = fct_relevel(Time_Point, c("Day 0", "Day 7", "Day 14")))

levels(RelAbund.Phyla.Filt.zerofilt$Time_Point)
```

Add column `Other_phyla` with the sum of all phyla that are not Bacteroidetes or Firmicutes

```{r}
RelAbund.Phyla.Filt.zerofilt.withother <- RelAbund.Phyla.Filt.zerofilt %>%
  mutate(Other_phyla = rowSums(select(.[6:ncol(.)], !contains(c("Bacteroidetes", "Firmicutes")))))

kable(head(RelAbund.Phyla.Filt.zerofilt.withother))
```

Add column B to F

```{r}
RelAbund.Phyla.Filt.zerofilt.withother.BtoF <- RelAbund.Phyla.Filt.zerofilt.withother %>%
  mutate(BtoF = Bacteroidetes/Firmicutes)

kable(head(RelAbund.Phyla.Filt.zerofilt.withother.BtoF))
```

Convert data from wide to long (i.e. make data [tidy](https://r4ds.had.co.nz/tidy-data.html))

```{r}
RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long <- RelAbund.Phyla.Filt.zerofilt.withother.BtoF %>%
  pivot_longer(cols = 6:ncol(.),
               names_to = "phylum",
               values_to = "rel_abund")

RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long[1:10,]
```

### Plotting

Stacked bar chart of B, F, and all the other phyla

```{r}
RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long.BFandOther <-
  RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long %>%
    filter(phylum %in% c("Bacteroidetes", "Firmicutes", "Other_phyla"))

head(RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long.BFandOther)

RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long.BFandOther %>%
  ggplot(aes(x=as.numeric(Pig), y=rel_abund, fill=phylum))+
  geom_col()+
  scale_fill_brewer(palette = "GnBu") +
  facet_grid(~Time_Point)+
  theme_classic()+
  labs(y="Relative Abundance", 
       fill="Phylum",
       x = "Pig") +
  theme(panel.grid = element_blank(), axis.text = element_text(color="black"),
        strip.text = element_text(color = "black", size = 14), 
        strip.background = element_blank())
```

### B to F Ratio

#### Plotting

B to F boxplot with jitter

```{r}
RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long.BtoF <- 
  RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long %>%
  filter(phylum == "BtoF")

head(RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long.BtoF)

BtoF_Boxplot <- RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long.BtoF %>%
  ggplot(aes(x=Diet, y=rel_abund, fill=Diet_By_Time_Point))+
  geom_boxplot(outlier.shape = NA)+
  geom_point(aes(fill = Diet_By_Time_Point), color = "black", alpha = 0.7, position=position_jitterdodge()) +
  scale_fill_manual(values = c("skyblue1", "dodgerblue", "royalblue4", 
                               "sienna1","firebrick3","tomato4")) +
  scale_color_manual(values = c("skyblue1", "dodgerblue", "royalblue4", 
                               "sienna1","firebrick3","tomato4")) +
  ylim(0, 1) +
  theme_minimal() +
  theme(axis.text.x = element_text(size = 12, color = "black"), 
        axis.text.y = element_text(color = "black"), 
        panel.grid.minor = element_blank()) +
  labs(x=NULL, 
       y= "Bacteroidota to Bacillota", 
       fill="Diet & Time Point",
       title = "Ratio of Bacteroidota to Bacillota") 

BtoF_Boxplot
```

Saving

```{r, eval = FALSE}
ggsave("Figures/BacteroidotatoBacilottaRatio_Boxplot.png", 
       plot = BtoF_Boxplot, 
       dpi = 800, 
       width = 7, 
       height = 5)
```

#### Statistics

```{r}
head(RelAbund.Phyla.Filt.zerofilt.withother.BtoF)

# select only columns used for ANOVA
RelAbund.Phyla.Filt.zerofilt.withother.BtoF.ForANOVA <- RelAbund.Phyla.Filt.zerofilt.withother.BtoF %>%
  select(Pig, Diet, Time_Point, BtoF)
```

Repeated measures ANOVA of B to F ratio

```{r}
BtoF.Ratio.ANOVA <- anova_test(data = RelAbund.Phyla.Filt.zerofilt.withother.BtoF.ForANOVA, 
                          formula = BtoF ~ Diet*Time_Point + Error(Pig/(Time_Point)),
                          dv = BtoF, wid = Pig, between = Diet, within = Time_Point)

get_anova_table(BtoF.Ratio.ANOVA)
```

* Significant effect of time point (p = 0.009)   
* Nonsignificant effect of diet (p = 0.728)   
* Nonsignificant effect of diet:timepoint (p = 0.436)   

Use posthoc to see where is significant using a fdr p-value adjustment for multiple testing, grouping by time (both diets)

```{r}
BtoF.Ratio.ANOVA.posthoc <- pairwise_t_test(BtoF ~ Time_Point, 
                                 data = RelAbund.Phyla.Filt.zerofilt.withother.BtoF, 
                                 paired = TRUE, 
                                 p.adjust.method = "fdr") 

BtoF.Ratio.ANOVA.posthoc
```

Significant difference is between day 0 and day 14 (padj = 0.016).

Use posthoc to see where is significant using a fdr p-value adjustment for multiple testing, separating by diet

```{r}
BtoF.Ratio.ANOVA.posthoc.bytime <- RelAbund.Phyla.Filt.zerofilt.withother.BtoF.ForANOVA %>%
  group_by(Diet) %>%
  pairwise_t_test(BtoF ~ Time_Point, 
                  paired = TRUE, 
                  p.adjust.method = "fdr")

BtoF.Ratio.ANOVA.posthoc.bytime
```

Significant in control between day 0 and 14, padj = 0.033. All else non-significant.

### Bacteroidetes and Firmicutes

#### Plotting

Boxplotting

```{r}
RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long.OnlyBandF <- 
  RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long %>%
    filter(phylum == "Bacteroidetes" | phylum == "Firmicutes")

btof.labs <- c("Bacteroidota", "Bacillota")
names(btof.labs) <- c("Bacteroidetes", "Firmicutes")

BandF_Boxplot <- RelAbund.Phyla.Filt.zerofilt.withother.BtoF.long.OnlyBandF %>%
  ggplot(aes(x=Diet, y=rel_abund, fill=Diet_By_Time_Point))+
  geom_boxplot(outlier.shape = NA)+
  geom_point(aes(fill = Diet_By_Time_Point), color = "black", position=position_jitterdodge(), alpha = 0.7) +
  scale_fill_manual(values=c("skyblue1", "dodgerblue", "royalblue4", "sienna1","firebrick3","tomato4"))+
  ylim(0, 0.75) +
  theme_bw() +
  facet_wrap(~phylum, labeller = labeller(phylum = btof.labs))+
  labs(x=NULL, y= "Relative Abundance", fill="Diet & Time Point") +
  theme(axis.text.x = element_text(size = 11, color = "black"), 
        axis.text.y = element_text(color = "black"), 
        panel.grid.minor = element_blank(), 
        strip.text.x = element_text(color = "black", size = 14),
        strip.background = element_rect(fill = "white"))

BandF_Boxplot
```

Saving

```{r, eval = FALSE}
ggsave("Figures/BacteroidotaBacilotta_Boxplot.png", 
       plot = BandF_Boxplot, 
       dpi = 800, 
       width = 7, 
       height = 5)
```

#### Statistics

##### Bacteroidetes

```{r}
# select only columns used for ANOVA
RelAbund.Phyla.Filt.zerofilt.withother.BtoF.Bonly <- RelAbund.Phyla.Filt.zerofilt.withother.BtoF %>%
  select(Pig, Diet, Time_Point, Bacteroidetes)
```

Bacteroidetes repeated measures ANOVA

```{r}
Bacteroidetes.ANOVA <- anova_test(data = RelAbund.Phyla.Filt.zerofilt.withother.BtoF.Bonly, 
                          formula = Bacteroidetes ~ Diet*Time_Point + Error(Pig/(Time_Point)),
                          dv = Bacteroidetes, wid = Pig, between = Diet, within = Time_Point)

get_anova_table(Bacteroidetes.ANOVA)
```

* Significant effect of time point (p = 0.024)   
* Nonsignificant effect of diet (p = 0.928)   
* onsignificant effect of diet:timepoint (p = 0.503)   

Use posthoc to see where is significant using a fdr p-value adjustment for multiple testing, grouping by time (both diets)

```{r}
Bacteroidetes.ANOVA.posthoc <- pairwise_t_test(Bacteroidetes ~ Time_Point, 
                                 data = RelAbund.Phyla.Filt.zerofilt.withother.BtoF, 
                                 paired = TRUE, 
                                 p.adjust.method = "fdr") 

Bacteroidetes.ANOVA.posthoc
```

Borderline significant difference is between day 0 and day 14 (padj = 0.058).

Use posthoc to see where is significant using a fdr p-value adjustment for multiple testing, separating by diet

```{r}
Bacteroidetes.ANOVA.posthoc.bytime <- RelAbund.Phyla.Filt.zerofilt.withother.BtoF.Bonly %>%
  group_by(Diet) %>%
  pairwise_t_test(Bacteroidetes ~ Time_Point, 
                  paired = TRUE, 
                  p.adjust.method = "fdr")

Bacteroidetes.ANOVA.posthoc.bytime
```

Significant difference in control between day 0 and 14, padj = 0.044. All else non-significant.

##### Firmicutes

```{r}
# select only columns used for ANOVA
RelAbund.Phyla.Filt.zerofilt.withother.BtoF.Fonly <- RelAbund.Phyla.Filt.zerofilt.withother.BtoF %>%
  select(Pig, Diet, Time_Point, Firmicutes)
```

Firmicutes repeated measures ANOVA

```{r}
Firmicutes.ANOVA <- anova_test(data = RelAbund.Phyla.Filt.zerofilt.withother.BtoF.Fonly, 
                          formula = Firmicutes ~ Diet*Time_Point + Error(Pig/(Time_Point)),
                          dv = Firmicutes, wid = Pig, between = Diet, within = Time_Point)

get_anova_table(Firmicutes.ANOVA)
```

* Significant effect of time point (p = 0.001)   
* Nonsignificant effect of diet (p = 0.313)   
* Nonsignificant effect of diet:timepoint (p = 0.380)   

Use posthoc to see where is significant using a fdr p-value adjustment for multiple testing, grouping by time (both diets)

```{r}
Firmicutes.ANOVA.posthoc <- pairwise_t_test(Firmicutes ~ Time_Point, 
                                 data = RelAbund.Phyla.Filt.zerofilt.withother.BtoF, 
                                 paired = TRUE, 
                                 p.adjust.method = "fdr") 

Firmicutes.ANOVA.posthoc
```

Significant difference is between day 0 and day 14 (padj = 0.003).

Use posthoc to see where is significant using a fdr p-value adjustment for multiple testing, separating by diet

```{r}
Firmicutes.ANOVA.posthoc.bytime <- RelAbund.Phyla.Filt.zerofilt.withother.BtoF.Fonly %>%
  group_by(Diet) %>%
  pairwise_t_test(Firmicutes ~ Time_Point, 
                  paired = TRUE, 
                  p.adjust.method = "fdr")

Firmicutes.ANOVA.posthoc.bytime
```

Significant difference is in control between day 0 and 14, padj = 0.030. All else non-significant.

## Alpha Diversity

Calculated alpha diversity of phyla based on relative abundance, including all the filtering for implausible phyla and removing samples with more than 33.33% missing samples

### Wrangling

```{r}
dim(RelAbund.Phyla.Filt.zerofilt)

RelAbund.Phyla.Filt.zerofilt[1:5,1:10]
```

Wrangle

```{r}
# move Sample_Name to rownames, remove metadata
RelAbund.Phyla.Filt.zerofilt.alphadiv <- RelAbund.Phyla.Filt.zerofilt

rownames(RelAbund.Phyla.Filt.zerofilt.alphadiv) <- RelAbund.Phyla.Filt.zerofilt.alphadiv$Sample_Name  

# remove metadata
RelAbund.Phyla.Filt.zerofilt.alphadiv <- RelAbund.Phyla.Filt.zerofilt.alphadiv %>%
  select(Acidobacteria:ncol(.))

RelAbund.Phyla.Filt.zerofilt.alphadiv[1:5,1:5]
```

### Calculate alpha diversity

```{r}
# run alpha diversity on phyla
phyla.filt.div <- diversity(RelAbund.Phyla.Filt.zerofilt.alphadiv, index = "shannon")

# convert to df
phyla.filt.div.df <- as.data.frame(phyla.filt.div)

# make column name 'shannon.phyla.filt'
colnames(phyla.filt.div.df) <- "shannon.phyla.filt"

head(phyla.filt.div.df)
```

Combine with metadata

```{r}
# combine with metadata
phyla.filt.div.df.meta <- cbind(RelAbund.Phyla.Filt.zerofilt[,1:5], phyla.filt.div.df)

head(phyla.filt.div.df.meta)
```

### Plotting

X-axis by diet

```{r}
alpha.diversity.phyla.bydiet <- phyla.filt.div.df.meta %>%
  ggplot(aes(x = Diet, y = shannon.phyla.filt, fill = Diet_By_Time_Point)) +
  geom_boxplot(outlier.shape = NA) +
  geom_point(aes(fill = Diet_By_Time_Point), 
             color = "black", 
             alpha = 0.7, 
             position=position_jitterdodge()) +
  scale_fill_manual(values = c("skyblue1", "dodgerblue", "royalblue4", 
                               "sienna1","firebrick3","tomato4")) +
  theme_minimal() +
  theme(axis.text.x = element_text(size = 12, color = "black")) +
  labs(x=NULL, 
       y="Shannon diversity index", 
       title = "Alpha Diversity",
       subtitle = "Shannon Index, Phyla Level", 
       fill="Diet & Time Point")

alpha.diversity.phyla.bydiet
```

```{r, eval = FALSE}
ggsave("Figures/AlphaDiversityPhyla_ByDiet_Boxplot.png", 
       plot = alpha.diversity.phyla.bydiet, 
       dpi = 800, 
       width = 10, 
       height = 6)
```

X-axis by time point

```{r}
alpha.diversity.phyla.bytime <- phyla.filt.div.df.meta %>%
  ggplot(aes(x = Time_Point, y = shannon.phyla.filt, fill = Diet_By_Time_Point)) +
  geom_boxplot(outlier.shape = NA) +
  geom_point(color = "black", alpha = 0.7, position=position_jitterdodge()) +
  scale_fill_manual(values = c("skyblue1", "dodgerblue", "royalblue4", 
                               "sienna1","firebrick3","tomato4")) +
  theme_minimal() +
  theme(axis.text.x = element_text(size = 12, color = "black")) +
  labs(x=NULL, 
       y="Shannon diversity index", 
       title = "Alpha Diversity",
       subtitle = "Shannon Index, Phyla Level", 
       fill="Diet & Time Point")

alpha.diversity.phyla.bytime
```

```{r, eval = FALSE}
ggsave("Figures/AlphaDiversityPhyla_ByTime_Boxplot.png", 
       plot = alpha.diversity.phyla.bytime, 
       dpi = 800, 
       width = 7, 
       height = 5)
```

### Statistics

Repeated measures ANOVA

```{r}
# must remove columns that aren't used in anova
head(phyla.filt.div.df.meta) 

phyla.filt.div.foranova <- phyla.filt.div.df.meta[,-c(1,5)]

head(phyla.filt.div.foranova)

phyla.filt.alphadiv.anova <- 
  anova_test(data = phyla.filt.div.foranova,
             formula = shannon.phyla.filt ~ Diet*Time_Point + Error(Pig/Time_Point),
             dv = shannon.phyla.filt, 
             wid = Pig, 
             within = Time_Point, 
             between = Diet)

get_anova_table(phyla.filt.alphadiv.anova)
```

* Significant effect of diet (p = 0.004)   
* Non-significant effect of timepoint (0.086)   
* Non-significant interaction of diet:time point (p = 0.791).   

Check for normality

```{r}
shapiro.test(phyla.filt.div.df.meta$shannon.phyla.filt)
```

Normal.

Post-hoc tests

```{r}
posthoc.morevariables <- phyla.filt.div.foranova %>%
  group_by(Time_Point) %>%
  anova_test(dv = shannon.phyla.filt, wid = Pig, between = Diet) %>%
  get_anova_table() %>%
  adjust_pvalue(method = "fdr")

posthoc.morevariables
```

Significant effect of diet at day 14 (padj = 0.024)

```{r}
posthoc.evenmorespecific <- phyla.filt.div.foranova %>%
  group_by(Time_Point) %>%
  pairwise_t_test(shannon.phyla.filt ~ Diet,
                  paired = TRUE,
                  p.adjust.method = "fdr")

posthoc.evenmorespecific
```

* Significant effect between control and tomato at day 14 (padj = 0.01)    
* Nonsignificant at day 0 (padj = 0.328)   
* Nonsignificant but getting close at day 7 (padj = 0.085)   

## ALDEx2

Quick introduction to anatomy of the aldex function 

The aldex function does every step - data transformation and statistics    
variable.name \<- aldex(reads.data, variables.vector, mc.samples=\#, test="t"/"kw", effect=T/F)   
reads.data - your reads/count data, un changed   
variables.vector - a vector of the variables corresponding to sample groups, in SAME order as sample names (and therefore columns)    
mc.samples - here you tell the function how many Monte Carlo sampels to use with an integer (128 is typical)    
test - which test do you want, t-test and wilcoxon, or anova-like and kruskal wallace? (will always do the parametric and non-parametric) t = t-test and wilcoxon kw = anova-like and kruskal wallace    
effect - do you want it to incude effect results in output?

Key to aldex outputs - taken directly from vignette

-   we.ep - Expected P value of Welch's t test
-   we.eBH - Expected Benjamini-Hochberg corrected P value of Welch's t test   
-   wi.ep - Expected P value of Wilcoxon rank test
-   wi.eBH - Expected Benjamini-Hochberg corrected P value of Wilcoxon test
-   kw.ep - Expected P value of Kruskal-Wallace test
-   kw.eBH - Expected Benjamini-Hochberg corrected P value of Kruskal-Wallace test
-   glm.ep - Expected P value of glm test
-   glm.eBH - Expected Benjamini-Hochberg corrected P value of glm test
-   rab.all - median clr value for all samples in the feature
-   rab.win.NS - median clr value for the NS group of samples
-   rab.win.S - median clr value for the S group of samples
-   dif.btw - median difference in clr values between S and NS groups
-   dif.win - median of the largest difference in clr values within S and NS groups
-   effect - median effect size: diff.btw / max(diff.win) for all instances
-   overlap - proportion of effect size that overlaps 0 (i.e. no effect)

ALDEx2 takes counts, not relative abundance.

We are using Benjamini Hochberg corrected pvalues, or `we.eBH` for t-tests (i.e., subsetting by time), and Benjamini-Hochberg corrected pvalues of the glm test `glm.eBH` for ANOVA tests (i.e., subsetting by diet)

Downloading ALDEx2

```{r, eval = FALSE}
if (!requireNamespace("BiocManager", quietly = TRUE))
  install.packages("BiocManager")

BiocManager::install("ALDEx2")
```

### Wrangling

Since we use counts for ALDEx2, we need to filter our counts data to include only the phyla we ended up using in our final analysis

```{r}
# this data set filtered to remove inplausible phyla, but still includes phyla with a lot of missing values 
Phyla.Counts.Filt[1:10,1:10]
dim(Phyla.Counts.Filt)

# final phyla list, after filtering for number of zeros
final_phyla[1:10,]

# how many final phyla do we have?
dim(final_phyla)

# join to create a df with phyla in rows, samples in columns
# filtered for genera used in this analysis
phyla_counts_foraldex <- inner_join(final_phyla, Phyla.Counts.Filt,
                                    by = "phylum")

dim(phyla_counts_foraldex)
phyla_counts_foraldex[1:10, 1:4]

# add phyla as rownames
rownames(phyla_counts_foraldex) <- phyla_counts_foraldex$phylum

# remove phylum, domain as columns for cleaner data
phyla_counts_foraldex <- phyla_counts_foraldex %>%
  select(-phylum, -domain)
```

### Subset by Time

#### Day 0

```{r}
# subset day 0 only
Day0.Counts.Phyla.filt <- phyla_counts_foraldex %>% 
  select(ends_with("Day0"))
```

ALDEx2 function needs a factor of variables

```{r}
# order alphabetically so making the meta data vector is easier
Day0.Counts.Phyla.filt <- Day0.Counts.Phyla.filt[order(colnames(Day0.Counts.Phyla.filt))]

Diets.Day0.Phyla <- as.vector(c(rep("Control", times=10), rep("Tomato", times=10)))

# check and make sure it came out right
Diets.Day0.Phyla
```

Run t-tests

```{r, cache = TRUE}
# runs very slowly
# set cache = TRUE to save results
filt.Phyla.Day0.ByDiet.aldex <- aldex(Day0.Counts.Phyla.filt, 
                                       Diets.Day0.Phyla, 
                                       mc.samples = 1000, 
                                       test = "t", 
                                       effect = TRUE)
```

```{r}
filt.Phyla.Day0.ByDiet.aldex <- 
  filt.Phyla.Day0.ByDiet.aldex[order(filt.Phyla.Day0.ByDiet.aldex$we.eBH, 
                                      decreasing = FALSE),]

kable(head(filt.Phyla.Day0.ByDiet.aldex))
```

No significantly different phyla

```{r}
hist(filt.Phyla.Day0.ByDiet.aldex$we.eBH,
     breaks = 45,
     main = "Histogram of p-values on the effect of diet at day 0 on phyla",
     xlab = "Benjamini Hochberg corrected p-value (we.eBH)")
```

`we.eBH` is the Benjamini-Hochberg corrected p-value, and nothing is \< 0.05

#### Day 7

```{r}
# subset day 7 only
Day7.Counts.Phyla.filt <- phyla_counts_foraldex %>% 
  select(ends_with("Day7"))
```

ALDEx2 function needs a factor of variables

```{r}
# order alphabetically so making the meta data vector is easier
Day7.Counts.Phyla.filt <- Day7.Counts.Phyla.filt[order(colnames(Day7.Counts.Phyla.filt))]

Diets.Day7.Phyla <- as.vector(c(rep("Control", times=10), rep("Tomato", times=10)))

# check and make sure it came out right
Diets.Day7.Phyla
```

Run t-tests

```{r, cache = TRUE}
# runs very slowly
# set cache = TRUE to save results
filt.Phyla.Day7.ByDiet.aldex <- aldex(Day7.Counts.Phyla.filt, 
                                       Diets.Day7.Phyla, 
                                       mc.samples = 1000, 
                                       test = "t", 
                                       effect = TRUE)
```

```{r}
filt.Phyla.Day7.ByDiet.aldex <- 
  filt.Phyla.Day7.ByDiet.aldex[order(filt.Phyla.Day7.ByDiet.aldex$we.eBH, 
                                      decreasing = FALSE),]

kable(head(filt.Phyla.Day7.ByDiet.aldex))
```

One phyla was significantly different by diet at day 7 - unclassified (derived from bacteria), padj = 0.002

```{r}
hist(filt.Phyla.Day7.ByDiet.aldex$we.eBH,
     breaks = 45,
     main = "Histogram of p-values on the effect of diet at day 7 on phyla",
     xlab = "Benjamini Hochberg corrected p-value (we.eBH)")
```

#### Day 14

```{r}
# subset day 14 only
Day14.Counts.Phyla.filt <- phyla_counts_foraldex %>% 
  select(ends_with("Day14"))
```

ALDEx2 function needs a factor of variables

```{r}
# order alphabetically so making the meta data vector is easier
Day14.Counts.Phyla.filt <- Day14.Counts.Phyla.filt[order(colnames(Day14.Counts.Phyla.filt))]

Diets.Day14.Phyla <- as.vector(c(rep("Control", times=10), rep("Tomato", times=10)))

# check and make sure it came out right
Diets.Day14.Phyla
```

Run t-tests

```{r, cache = TRUE}
filt.Phyla.Day14.ByDiet.aldex <- aldex(Day14.Counts.Phyla.filt, 
                                       Diets.Day14.Phyla, 
                                       mc.samples = 1000, 
                                       test = "t", 
                                       effect = TRUE)
```

```{r}
filt.Phyla.Day14.ByDiet.aldex <- 
  filt.Phyla.Day14.ByDiet.aldex[order(filt.Phyla.Day14.ByDiet.aldex$we.eBH, 
                                      decreasing = FALSE),]

kable(head(filt.Phyla.Day14.ByDiet.aldex))
```

```{r}
hist(filt.Phyla.Day14.ByDiet.aldex$we.eBH,
     breaks = 45,
     main = "Histogram of p-values on the effect of diet at day 14 on phyla",
     xlab = "Benjamini Hochberg corrected p-value (we.eBH)")
```

How many significant phyla are there?

```{r}
filt.Phyla.Day14.ByDiet.aldex.sig <- 
  filt.Phyla.Day14.ByDiet.aldex[which(filt.Phyla.Day14.ByDiet.aldex$we.eBH<0.05),]

length(rownames(filt.Phyla.Day14.ByDiet.aldex.sig))
```

5 sig phyla

Which phyla are they?

```{r}
sig_day14_phyla_aldex2 <- as.data.frame(cbind(rownames(filt.Phyla.Day14.ByDiet.aldex.sig),
                                 filt.Phyla.Day14.ByDiet.aldex.sig$we.eBH))

sig_day14_phyla_aldex2
```

* unclassified (derived from Bacteria)
* Nematoda
* Apicomplexa
* Deinococcus-Thermus
* Proteobacteria

What is the directionality of the change?

```{r}
filt.Phyla.Day14.ByDiet.aldex %>%
  select(rab.win.Control, rab.win.Tomato, we.eBH) %>%
  filter(we.eBH <= 0.05)
```

Higher in control:   
* Deinococcus-Thermus

Higher in tomato:   
* unclassified (derived from Bacteria)   
* Nematoda   
* Apicomplexa   
* Proteobacteria   

### Subset by diet

#### Control

```{r}
# subset control only samples across all time points, should be n=30
Control.Counts.Phyla.filt <- phyla_counts_foraldex %>% 
  select(contains("Control"))
```

ALDEx2 function needs a factor of variables

```{r}
# results in pigs at different time points being grouped together
Control.Counts.Phyla.filt <- Control.Counts.Phyla.filt[order(colnames(Control.Counts.Phyla.filt))]

# then time point by "alphabetical" where 14 comes before 7
# ex, first few are Pig 10 Day 0, Pig 10 Day 14, Pig 10 Day 7, Pig 1 Day 0, Pig 1 Day 14, etc

TimePoints.Control.Phyla <- as.vector(rep(c("Day0", "Day14", "Day7"), times=10))

# check and make sure it looks right
TimePoints.Control.Phyla
```

More than two conditions this time, use the ANOVA-like test

```{r, cache = TRUE}
filt.Phyla.Control.ByTime.aldex <- aldex(Control.Counts.Phyla.filt, 
                                          TimePoints.Control.Phyla, 
                                          mc.samples = 1000, 
                                          test = "kw", 
                                          effect = FALSE)
```

We are looking at `glm.eBH` for the BH corrected ANOVA pval

```{r}
filt.Phyla.Control.ByTime.aldex <- 
  filt.Phyla.Control.ByTime.aldex[order(filt.Phyla.Control.ByTime.aldex$glm.eBH, 
                                         decreasing = FALSE),]

kable(head(filt.Phyla.Control.ByTime.aldex))
```

```{r}
hist(filt.Phyla.Control.ByTime.aldex$glm.eBH,
     breaks = 45,
     main = "Histogram of p-values on the effect of time on control pigs on phyla",
     xlab = "Benjamini Hochberg corrected p-value (glm.eBH)")
```

How many significant phyla are there?

```{r}
filt.Phyla.Control.ByTime.aldex.sig <- 
  filt.Phyla.Control.ByTime.aldex[which(filt.Phyla.Control.ByTime.aldex$glm.eBH<0.05),]

length(rownames(filt.Phyla.Control.ByTime.aldex.sig))
```

0 sig phyla

#### Tomato

```{r}
# subset tomato only samples across all time points, should be n=30
Tomato.Counts.Phyla.filt <- phyla_counts_foraldex %>% 
  select(contains("Tomato"))
```

ALDEx2 function needs a factor of variables

```{r}
# results in pigs at different time points being grouped together
Tomato.Counts.Phyla.filt <- Tomato.Counts.Phyla.filt[order(colnames(Tomato.Counts.Phyla.filt))]

# then time point by "alphabetical" where 14 comes before 7
# ex, first few are Pig 10 Day 0, Pig 10 Day 14, Pig 10 Day 7, Pig 1 Day 0, Pig 1 Day 14, etc

TimePoints.Tomato.Phyla <- as.vector(rep(c("Day0", "Day14", "Day7"), times=10))

# check and make sure it looks right
TimePoints.Tomato.Phyla
```

More than two conditions this time, use the ANOVA-like test

```{r, cache = TRUE}
filt.Phyla.Tomato.ByTime.aldex <- aldex(Tomato.Counts.Phyla.filt, 
                                          TimePoints.Tomato.Phyla, 
                                          mc.samples = 1000, 
                                          test = "kw", 
                                          effect = FALSE)
```

We are looking at glm.eBH for the BH corrected ANOVA pval

```{r}
filt.Phyla.Tomato.ByTime.aldex <- 
  filt.Phyla.Tomato.ByTime.aldex[order(filt.Phyla.Tomato.ByTime.aldex$glm.eBH, 
                                         decreasing = FALSE),]

kable(head(filt.Phyla.Tomato.ByTime.aldex))
```

```{r}
hist(filt.Phyla.Tomato.ByTime.aldex$glm.eBH,
     breaks = 45,
     main = "Histogram of p-values on the effect of time on tomato pigs on phyla",
     xlab = "Benjamini Hochberg corrected p-value (glm.eBH)")
```

How many significant phyla are there?

```{r}
filt.Phyla.Tomato.ByTime.aldex.sig <- 
  filt.Phyla.Tomato.ByTime.aldex[which(filt.Phyla.Tomato.ByTime.aldex$glm.eBH<0.05),]

length(rownames(filt.Phyla.Tomato.ByTime.aldex.sig))
```

1 sig phyla, unclassified (derived from Bacteria)
